Preparation

Clean the environment.

Set locations, and the working directory.

A package-installation function.

Load those packages.

We will create a datestamp and define the Utrecht Science Park Colour Scheme.

# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    tvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    R = summary(fit)$r.squared
    R.adj = summary(fit)$adj.r.squared
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
    
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("T-value...................:", round(tvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("R^2.......................:", round(R, 6), "\n")
    cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,9))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data...\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    zvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    dev <- fit$deviance
    nullDev <- fit$null.deviance
    modelN <- length(fit$fitted.values)
    R.l <- 1 - dev / nullDev
    R.cs <- 1 - exp(-(nullDev - dev) / modelN)
    R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("Z-value...................:", round(zvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
    cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
    cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b). While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.

Objectives

Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.

Methods

Blood

  • IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]
  • MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. Measured at the WKZ. Recalculated Luminex. [pg/mL]

Plaque

  • IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug], measured by Luminex at the WKZ.
  • MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug], measured by Luminex at the WKZ.
  • IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug], measured by Luminex at the WKZ.

Loading data

Clinical data

Loading Athero-Express clinical data.

require(haven)

AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))

head(AEDB)

require(openxlsx)
Loading required package: openxlsx
AEDB_ProtConc <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_Vriezer/20190919_Freezers_preTCBio.xlsx"), 
                                     sheet = "ProteinConc.", 
                                     skipEmptyCols = TRUE)

AEDB_Blood <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_Vriezer/20190919_Freezers_preTCBio.xlsx"), 
                                     sheet = "Blood", 
                                     skipEmptyCols = TRUE)
AEDB_Plaque <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_Vriezer/20190919_Freezers_preTCBio.xlsx"), 
                                     sheet = "Plaque", 
                                     skipEmptyCols = TRUE)

head(AEDB_ProtConc)
head(AEDB_Blood)
head(AEDB_Plaque)
NA
NA

Fixing and creating variables

We need to be very strict in defining symptoms. Therefore we will fix a new variable that groups symptoms at inclusion.

Coding of symptoms is as follows:

  • missing -999
  • Asymptomatic 0
  • TIA 1
  • minor stroke 2
  • Major stroke 3
  • Amaurosis fugax 4
  • Four vessel disease 5
  • Vertebrobasilary TIA 7
  • Retinal infarction 8
  • Symptomatic, but aspecific symtoms 9
  • Contralateral symptomatic occlusion 10
  • retinal infarction 11
  • armclaudication due to occlusion subclavian artery, CEA needed for bypass 12
  • retinal infarction + TIAs 13
  • Ocular ischemic syndrome 14
  • ischemisch glaucoom 15
  • subclavian steal syndrome 16
  • TGA 17

We will group as follows in Symptoms.5G:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13
  3. Stroke > 2, 3
  4. Ocular > 4, 14, 15
  5. Retinal infarction > 8, 11
  6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in AsymptSympt:

  1. Asymptomatic > 0
  2. TIA > 1, 7, 13 + Stroke > 2, 3
  3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
# Fix symptoms
attach(AEDB)
# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"
detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
require(labelled)
Loading required package: labelled
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)


table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
                    
                     Asymptomatic Ocular and others Symptomatic
  Asymptomatic                311                 0           0
  Ocular                        0               408           0
  Other                         0               118           0
  Retinal infarction            0                42           0
  Stroke                        0                 0         720
  TIA                           0                 0        1030
rm(AEDB.temp)

We will also fix the plaquephenotypes variable.

Coding of symptoms is as follows:

  • missing -999
  • not relevant -888
  • fibrous 1
  • fibroatheromatous 2
  • atheromatous 3

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)


rm(AEDB.temp)

We will also fix the diabetes status variable.


# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)


rm(AEDB.temp)

Athero-Express Biobank Study

Baseline characteristics

Showing the baseline table of the whole Athero-Express Biobank.

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]
                                     
                                      level                                                                     Overall                
  n                                                                                                                   2388             
  Hospital % (freq)                   St. Antonius, Nieuwegein                                                        39.8 ( 951)      
                                      UMC Utrecht                                                                     60.2 (1437)      
  Age (mean (SD))                                                                                                   69.119 (9.206)     
  Gender % (freq)                     female                                                                          30.4 ( 727)      
                                      male                                                                            69.6 (1661)      
  TC_final (mean (SD))                                                                                               4.790 (1.464)     
  LDL_final (mean (SD))                                                                                              2.813 (1.082)     
  HDL_final (mean (SD))                                                                                              1.201 (0.443)     
  TG_final (mean (SD))                                                                                               1.704 (1.038)     
  hsCRP_plasma (mean (SD))                                                                                          20.175 (231.417)   
  systolic (mean (SD))                                                                                             152.714 (25.196)    
  diastoli (mean (SD))                                                                                              81.485 (25.318)    
  GFR_MDRD (mean (SD))                                                                                              73.022 (21.193)    
  BMI (mean (SD))                                                                                                   26.503 (3.957)     
  KDOQI % (freq)                      No data available/missing                                                        0.0 (   0)      
                                      Normal kidney function                                                          18.8 ( 450)      
                                      CKD 2 (Mild)                                                                    51.2 (1223)      
                                      CKD 3 (Moderate)                                                                22.9 ( 546)      
                                      CKD 4 (Severe)                                                                   1.3 (  31)      
                                      CKD 5 (Failure)                                                                  0.4 (  10)      
                                      <NA>                                                                             5.4 ( 128)      
  BMI_WHO % (freq)                    No data available/missing                                                        0.0 (   0)      
                                      Underweight                                                                      1.0 (  23)      
                                      Normal                                                                          34.2 ( 816)      
                                      Overweight                                                                      43.3 (1033)      
                                      Obese                                                                           14.4 ( 343)      
                                      <NA>                                                                             7.2 ( 173)      
  SmokerCurrent % (freq)              no data available/missing                                                        0.0 (   0)      
                                      no                                                                              65.1 (1555)      
                                      yes                                                                             33.5 ( 801)      
                                      <NA>                                                                             1.3 (  32)      
  eCigarettes (mean (SD))                                                                                       173121.383 (152646.899)
  ePackYearsSmoking (mean (SD))                                                                                     23.715 (20.911)    
  DiabetesStatus % (freq)             Control (no Diabetes Dx/Med)                                                    76.0 (1815)      
                                      Diabetes                                                                        23.8 ( 568)      
                                      <NA>                                                                             0.2 (   5)      
  Hypertension.composite % (freq)     No data available/missing                                                        0.0 (   0)      
                                      no                                                                              14.7 ( 351)      
                                      yes                                                                             85.1 (2031)      
                                      <NA>                                                                             0.3 (   6)      
  Hypertension.drugs % (freq)         No data available/missing                                                        0.0 (   0)      
                                      no                                                                              23.6 ( 564)      
                                      yes                                                                             76.0 (1814)      
                                      <NA>                                                                             0.4 (  10)      
  Med.anticoagulants % (freq)         No data available/missing                                                        0.0 (   0)      
                                      no                                                                              88.1 (2104)      
                                      yes                                                                             11.3 ( 271)      
                                      <NA>                                                                             0.5 (  13)      
  Med.all.antiplatelet % (freq)       No data available/missing                                                        0.0 (   0)      
                                      no                                                                              12.4 ( 295)      
                                      yes                                                                             87.1 (2080)      
                                      <NA>                                                                             0.5 (  13)      
  Med.Statin.LLD % (freq)             No data available/missing                                                        0.0 (   0)      
                                      no                                                                              20.6 ( 492)      
                                      yes                                                                             78.9 (1885)      
                                      <NA>                                                                             0.5 (  11)      
  Stroke_Dx % (freq)                  Missing                                                                          0.0 (   0)      
                                      No stroke diagnosed                                                             71.0 (1696)      
                                      Stroke diagnosed                                                                20.9 ( 500)      
                                      <NA>                                                                             8.0 ( 192)      
  sympt % (freq)                      missing                                                                          0.0 (   0)      
                                      Asymptomatic                                                                    11.1 ( 266)      
                                      TIA                                                                             39.5 ( 944)      
                                      minor stroke                                                                    16.8 ( 402)      
                                      Major stroke                                                                     9.6 ( 229)      
                                      Amaurosis fugax                                                                 15.5 ( 369)      
                                      Four vessel disease                                                              1.6 (  38)      
                                      Vertebrobasilary TIA                                                             0.2 (   5)      
                                      Retinal infarction                                                               1.4 (  33)      
                                      Symptomatic, but aspecific symtoms                                               2.2 (  52)      
                                      Contralateral symptomatic occlusion                                              0.5 (  11)      
                                      retinal infarction                                                               0.3 (   6)      
                                      armclaudication due to occlusion subclavian artery, CEA needed for bypass        0.0 (   1)      
                                      retinal infarction + TIAs                                                        0.1 (   2)      
                                      Ocular ischemic syndrome                                                         0.8 (  18)      
                                      ischemisch glaucoom                                                              0.0 (   0)      
                                      subclavian steal syndrome                                                        0.0 (   1)      
                                      TGA                                                                              0.0 (   0)      
                                      <NA>                                                                             0.5 (  11)      
  Symptoms.5G % (freq)                Asymptomatic                                                                    11.1 ( 266)      
                                      Ocular                                                                          16.2 ( 387)      
                                      Other                                                                            4.3 ( 103)      
                                      Retinal infarction                                                               1.6 (  39)      
                                      Stroke                                                                          26.4 ( 631)      
                                      TIA                                                                             39.8 ( 951)      
                                      <NA>                                                                             0.5 (  11)      
  AsymptSympt % (freq)                Asymptomatic                                                                    11.1 ( 266)      
                                      Ocular and others                                                               22.2 ( 529)      
                                      Symptomatic                                                                     66.2 (1582)      
                                      <NA>                                                                             0.5 (  11)      
  restenos % (freq)                   missing                                                                          0.0 (   0)      
                                      de novo                                                                         93.6 (2235)      
                                      restenosis                                                                       4.9 ( 117)      
                                      stenose bij angioseal na PTCA                                                    0.0 (   0)      
                                      <NA>                                                                             1.5 (  36)      
  stenose % (freq)                    missing                                                                          0.0 (   0)      
                                      0-49%                                                                            0.5 (  13)      
                                      50-70%                                                                           7.6 ( 181)      
                                      70-90%                                                                          46.9 (1121)      
                                      90-99%                                                                          38.4 ( 918)      
                                      100% (Occlusion)                                                                 1.5 (  35)      
                                      NA                                                                               0.0 (   0)      
                                      50-99%                                                                           0.6 (  14)      
                                      70-99%                                                                           2.3 (  55)      
                                      99                                                                               0.1 (   2)      
                                      <NA>                                                                             2.1 (  49)      
  EP_composite % (freq)               No data available.                                                               0.0 (   0)      
                                      No composite endpoints                                                          70.5 (1684)      
                                      Composite endpoints                                                             24.2 ( 578)      
                                      <NA>                                                                             5.3 ( 126)      
  EP_composite_time (mean (SD))                                                                                      2.494 (1.102)     
  macmean0 (mean (SD))                                                                                               0.769 (1.186)     
  smcmean0 (mean (SD))                                                                                               1.982 (2.378)     
  Macrophages.bin % (freq)            no/minor                                                                        35.6 ( 850)      
                                      moderate/heavy                                                                  41.7 ( 995)      
                                      <NA>                                                                            22.7 ( 543)      
  SMC.bin % (freq)                    no/minor                                                                        25.3 ( 605)      
                                      moderate/heavy                                                                  52.2 (1247)      
                                      <NA>                                                                            22.4 ( 536)      
  neutrophils (mean (SD))                                                                                          146.685 (419.386)   
  Mast_cells_plaque (mean (SD))                                                                                    164.488 (163.771)   
  IPH.bin % (freq)                    no                                                                              31.3 ( 747)      
                                      yes                                                                             46.5 (1111)      
                                      <NA>                                                                            22.2 ( 530)      
  vessel_density_averaged (mean (SD))                                                                                8.322 (6.386)     
  Calc.bin % (freq)                   no/minor                                                                        42.1 (1005)      
                                      moderate/heavy                                                                  35.7 ( 852)      
                                      <NA>                                                                            22.2 ( 531)      
  Collagen.bin % (freq)               no/minor                                                                        16.0 ( 381)      
                                      moderate/heavy                                                                  61.6 (1472)      
                                      <NA>                                                                            22.4 ( 535)      
  Fat.bin_10 % (freq)                  <10%                                                                           22.7 ( 541)      
                                       >10%                                                                           55.3 (1321)      
                                      <NA>                                                                            22.0 ( 526)      
  Fat.bin_40 % (freq)                 <40%                                                                            57.2 (1365)      
                                      >40%                                                                            20.8 ( 497)      
                                      <NA>                                                                            22.0 ( 526)      
  OverallPlaquePhenotype % (freq)     atheromatous                                                                    20.1 ( 481)      
                                      fibroatheromatous                                                               28.4 ( 677)      
                                      fibrous                                                                         29.1 ( 696)      
                                      <NA>                                                                            22.4 ( 534)      
  IL6_pg_ug_2015 (mean (SD))                                                                                         0.138 (0.556)     
  MCP1_pg_ug_2015 (mean (SD))                                                                                        0.612 (0.904)     
                                     
                                      Missing
  n                                          
  Hospital % (freq)                    0.0   
                                             
  Age (mean (SD))                      0.0   
  Gender % (freq)                      0.0   
                                             
  TC_final (mean (SD))                38.7   
  LDL_final (mean (SD))               46.4   
  HDL_final (mean (SD))               42.4   
  TG_final (mean (SD))                43.5   
  hsCRP_plasma (mean (SD))            52.1   
  systolic (mean (SD))                11.6   
  diastoli (mean (SD))                11.6   
  GFR_MDRD (mean (SD))                 5.3   
  BMI (mean (SD))                      7.2   
  KDOQI % (freq)                       5.4   
                                             
                                             
                                             
                                             
                                             
                                             
  BMI_WHO % (freq)                     7.2   
                                             
                                             
                                             
                                             
                                             
  SmokerCurrent % (freq)               1.3   
                                             
                                             
                                             
  eCigarettes (mean (SD))             12.3   
  ePackYearsSmoking (mean (SD))       12.3   
  DiabetesStatus % (freq)              0.2   
                                             
                                             
  Hypertension.composite % (freq)      0.3   
                                             
                                             
                                             
  Hypertension.drugs % (freq)          0.4   
                                             
                                             
                                             
  Med.anticoagulants % (freq)          0.5   
                                             
                                             
                                             
  Med.all.antiplatelet % (freq)        0.5   
                                             
                                             
                                             
  Med.Statin.LLD % (freq)              0.5   
                                             
                                             
                                             
  Stroke_Dx % (freq)                   8.0   
                                             
                                             
                                             
  sympt % (freq)                       0.5   
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
  Symptoms.5G % (freq)                 0.5   
                                             
                                             
                                             
                                             
                                             
                                             
  AsymptSympt % (freq)                 0.5   
                                             
                                             
                                             
  restenos % (freq)                    1.5   
                                             
                                             
                                             
                                             
  stenose % (freq)                     2.1   
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
  EP_composite % (freq)                5.3   
                                             
                                             
                                             
  EP_composite_time (mean (SD))        5.4   
  macmean0 (mean (SD))                28.4   
  smcmean0 (mean (SD))                28.6   
  Macrophages.bin % (freq)            22.7   
                                             
                                             
  SMC.bin % (freq)                    22.4   
                                             
                                             
  neutrophils (mean (SD))             87.2   
  Mast_cells_plaque (mean (SD))       89.9   
  IPH.bin % (freq)                    22.2   
                                             
                                             
  vessel_density_averaged (mean (SD)) 34.0   
  Calc.bin % (freq)                   22.2   
                                             
                                             
  Collagen.bin % (freq)               22.4   
                                             
                                             
  Fat.bin_10 % (freq)                 22.0   
                                             
                                             
  Fat.bin_40 % (freq)                 22.0   
                                             
                                             
  OverallPlaquePhenotype % (freq)     22.4   
                                             
                                             
                                             
  IL6_pg_ug_2015 (mean (SD))          51.7   
  MCP1_pg_ug_2015 (mean (SD))         49.7   
AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(IL6_pg_ug_2015) | !is.na(MCP1_pg_ug_2015))

AEDB.CEA.subset.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "DiabetesStatus",
                                         data = AEDB.CEA.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]
                                     
                                      level                                                                     Overall                
  n                                                                                                                   1201             
  Hospital % (freq)                   St. Antonius, Nieuwegein                                                        46.9 ( 563)      
                                      UMC Utrecht                                                                     53.1 ( 638)      
  Age (mean (SD))                                                                                                   68.674 (9.167)     
  Gender % (freq)                     female                                                                          30.6 ( 367)      
                                      male                                                                            69.4 ( 834)      
  TC_final (mean (SD))                                                                                               4.730 (1.250)     
  LDL_final (mean (SD))                                                                                              2.812 (1.055)     
  HDL_final (mean (SD))                                                                                              1.180 (0.470)     
  TG_final (mean (SD))                                                                                               1.656 (0.964)     
  hsCRP_plasma (mean (SD))                                                                                          15.273 (107.100)   
  systolic (mean (SD))                                                                                             155.628 (26.036)    
  diastoli (mean (SD))                                                                                              82.644 (13.533)    
  GFR_MDRD (mean (SD))                                                                                              71.764 (20.074)    
  BMI (mean (SD))                                                                                                   26.355 (3.706)     
  KDOQI % (freq)                      No data available/missing                                                        0.0 (   0)      
                                      Normal kidney function                                                          17.2 ( 207)      
                                      CKD 2 (Mild)                                                                    53.0 ( 636)      
                                      CKD 3 (Moderate)                                                                24.7 ( 297)      
                                      CKD 4 (Severe)                                                                   1.1 (  13)      
                                      CKD 5 (Failure)                                                                  0.4 (   5)      
                                      <NA>                                                                             3.6 (  43)      
  BMI_WHO % (freq)                    No data available/missing                                                        0.0 (   0)      
                                      Underweight                                                                      0.9 (  11)      
                                      Normal                                                                          35.2 ( 423)      
                                      Overweight                                                                      46.7 ( 561)      
                                      Obese                                                                           12.7 ( 153)      
                                      <NA>                                                                             4.4 (  53)      
  SmokerCurrent % (freq)              no data available/missing                                                        0.0 (   0)      
                                      no                                                                              63.0 ( 757)      
                                      yes                                                                             35.4 ( 425)      
                                      <NA>                                                                             1.6 (  19)      
  eCigarettes (mean (SD))                                                                                       171480.910 (153112.295)
  ePackYearsSmoking (mean (SD))                                                                                     23.491 (20.974)    
  DiabetesStatus % (freq)             Control (no Diabetes Dx/Med)                                                    77.2 ( 927)      
                                      Diabetes                                                                        22.8 ( 274)      
  Hypertension.composite % (freq)     No data available/missing                                                        0.0 (   0)      
                                      no                                                                              13.8 ( 166)      
                                      yes                                                                             86.2 (1035)      
  Hypertension.drugs % (freq)         No data available/missing                                                        0.0 (   0)      
                                      no                                                                              22.5 ( 270)      
                                      yes                                                                             77.4 ( 929)      
                                      <NA>                                                                             0.2 (   2)      
  Med.anticoagulants % (freq)         No data available/missing                                                        0.0 (   0)      
                                      no                                                                              88.0 (1057)      
                                      yes                                                                             11.8 ( 142)      
                                      <NA>                                                                             0.2 (   2)      
  Med.all.antiplatelet % (freq)       No data available/missing                                                        0.0 (   0)      
                                      no                                                                              10.5 ( 126)      
                                      yes                                                                             89.1 (1070)      
                                      <NA>                                                                             0.4 (   5)      
  Med.Statin.LLD % (freq)             No data available/missing                                                        0.0 (   0)      
                                      no                                                                              21.9 ( 263)      
                                      yes                                                                             77.9 ( 936)      
                                      <NA>                                                                             0.2 (   2)      
  Stroke_Dx % (freq)                  Missing                                                                          0.0 (   0)      
                                      No stroke diagnosed                                                             75.6 ( 908)      
                                      Stroke diagnosed                                                                19.1 ( 229)      
                                      <NA>                                                                             5.3 (  64)      
  sympt % (freq)                      missing                                                                          0.0 (   0)      
                                      Asymptomatic                                                                    10.9 ( 131)      
                                      TIA                                                                             41.2 ( 495)      
                                      minor stroke                                                                    14.8 ( 178)      
                                      Major stroke                                                                    10.8 ( 130)      
                                      Amaurosis fugax                                                                 15.2 ( 183)      
                                      Four vessel disease                                                              1.9 (  23)      
                                      Vertebrobasilary TIA                                                             0.2 (   2)      
                                      Retinal infarction                                                               1.2 (  15)      
                                      Symptomatic, but aspecific symtoms                                               2.4 (  29)      
                                      Contralateral symptomatic occlusion                                              0.5 (   6)      
                                      retinal infarction                                                               0.2 (   3)      
                                      armclaudication due to occlusion subclavian artery, CEA needed for bypass        0.1 (   1)      
                                      retinal infarction + TIAs                                                        0.0 (   0)      
                                      Ocular ischemic syndrome                                                         0.1 (   1)      
                                      ischemisch glaucoom                                                              0.0 (   0)      
                                      subclavian steal syndrome                                                        0.0 (   0)      
                                      TGA                                                                              0.0 (   0)      
                                      <NA>                                                                             0.3 (   4)      
  Symptoms.5G % (freq)                Asymptomatic                                                                    10.9 ( 131)      
                                      Ocular                                                                          15.3 ( 184)      
                                      Other                                                                            4.9 (  59)      
                                      Retinal infarction                                                               1.5 (  18)      
                                      Stroke                                                                          25.6 ( 308)      
                                      TIA                                                                             41.4 ( 497)      
                                      <NA>                                                                             0.3 (   4)      
  AsymptSympt % (freq)                Asymptomatic                                                                    10.9 ( 131)      
                                      Ocular and others                                                               21.7 ( 261)      
                                      Symptomatic                                                                     67.0 ( 805)      
                                      <NA>                                                                             0.3 (   4)      
  restenos % (freq)                   missing                                                                          0.0 (   0)      
                                      de novo                                                                         94.6 (1136)      
                                      restenosis                                                                       3.1 (  37)      
                                      stenose bij angioseal na PTCA                                                    0.0 (   0)      
                                      <NA>                                                                             2.3 (  28)      
  stenose % (freq)                    missing                                                                          0.0 (   0)      
                                      0-49%                                                                            0.5 (   6)      
                                      50-70%                                                                           6.0 (  72)      
                                      70-90%                                                                          45.3 ( 544)      
                                      90-99%                                                                          42.6 ( 512)      
                                      100% (Occlusion)                                                                 0.8 (  10)      
                                      NA                                                                               0.0 (   0)      
                                      50-99%                                                                           0.4 (   5)      
                                      70-99%                                                                           1.2 (  14)      
                                      99                                                                               0.0 (   0)      
                                      <NA>                                                                             3.2 (  38)      
  EP_composite % (freq)               No data available.                                                               0.0 (   0)      
                                      No composite endpoints                                                          73.5 ( 883)      
                                      Composite endpoints                                                             25.6 ( 308)      
                                      <NA>                                                                             0.8 (  10)      
  EP_composite_time (mean (SD))                                                                                      2.623 (1.112)     
  macmean0 (mean (SD))                                                                                               0.790 (1.220)     
  smcmean0 (mean (SD))                                                                                               1.934 (2.185)     
  Macrophages.bin % (freq)            no/minor                                                                        47.5 ( 570)      
                                      moderate/heavy                                                                  50.6 ( 608)      
                                      <NA>                                                                             1.9 (  23)      
  SMC.bin % (freq)                    no/minor                                                                        31.2 ( 375)      
                                      moderate/heavy                                                                  66.9 ( 804)      
                                      <NA>                                                                             1.8 (  22)      
  neutrophils (mean (SD))                                                                                          170.898 (479.879)   
  Mast_cells_plaque (mean (SD))                                                                                    172.458 (173.290)   
  IPH.bin % (freq)                    no                                                                              38.3 ( 460)      
                                      yes                                                                             60.0 ( 720)      
                                      <NA>                                                                             1.7 (  21)      
  vessel_density_averaged (mean (SD))                                                                                8.426 (6.475)     
  Calc.bin % (freq)                   no/minor                                                                        50.1 ( 602)      
                                      moderate/heavy                                                                  48.1 ( 578)      
                                      <NA>                                                                             1.7 (  21)      
  Collagen.bin % (freq)               no/minor                                                                        20.7 ( 249)      
                                      moderate/heavy                                                                  77.7 ( 933)      
                                      <NA>                                                                             1.6 (  19)      
  Fat.bin_10 % (freq)                  <10%                                                                           26.8 ( 322)      
                                       >10%                                                                           71.6 ( 860)      
                                      <NA>                                                                             1.6 (  19)      
  Fat.bin_40 % (freq)                 <40%                                                                            71.6 ( 860)      
                                      >40%                                                                            26.8 ( 322)      
                                      <NA>                                                                             1.6 (  19)      
  OverallPlaquePhenotype % (freq)     atheromatous                                                                    26.7 ( 321)      
                                      fibroatheromatous                                                               35.9 ( 431)      
                                      fibrous                                                                         35.6 ( 427)      
                                      <NA>                                                                             1.8 (  22)      
  IL6_pg_ug_2015 (mean (SD))                                                                                         0.138 (0.556)     
  MCP1_pg_ug_2015 (mean (SD))                                                                                        0.612 (0.904)     
                                     
                                      Missing
  n                                          
  Hospital % (freq)                    0.0   
                                             
  Age (mean (SD))                      0.0   
  Gender % (freq)                      0.0   
                                             
  TC_final (mean (SD))                33.6   
  LDL_final (mean (SD))               39.8   
  HDL_final (mean (SD))               36.6   
  TG_final (mean (SD))                36.2   
  hsCRP_plasma (mean (SD))            38.8   
  systolic (mean (SD))                14.0   
  diastoli (mean (SD))                14.0   
  GFR_MDRD (mean (SD))                 3.5   
  BMI (mean (SD))                      4.2   
  KDOQI % (freq)                       3.6   
                                             
                                             
                                             
                                             
                                             
                                             
  BMI_WHO % (freq)                     4.4   
                                             
                                             
                                             
                                             
                                             
  SmokerCurrent % (freq)               1.6   
                                             
                                             
                                             
  eCigarettes (mean (SD))              9.8   
  ePackYearsSmoking (mean (SD))        9.8   
  DiabetesStatus % (freq)              0.0   
                                             
  Hypertension.composite % (freq)      0.0   
                                             
                                             
  Hypertension.drugs % (freq)          0.2   
                                             
                                             
                                             
  Med.anticoagulants % (freq)          0.2   
                                             
                                             
                                             
  Med.all.antiplatelet % (freq)        0.4   
                                             
                                             
                                             
  Med.Statin.LLD % (freq)              0.2   
                                             
                                             
                                             
  Stroke_Dx % (freq)                   5.3   
                                             
                                             
                                             
  sympt % (freq)                       0.3   
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
  Symptoms.5G % (freq)                 0.3   
                                             
                                             
                                             
                                             
                                             
                                             
  AsymptSympt % (freq)                 0.3   
                                             
                                             
                                             
  restenos % (freq)                    2.3   
                                             
                                             
                                             
                                             
  stenose % (freq)                     3.2   
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
  EP_composite % (freq)                0.8   
                                             
                                             
                                             
  EP_composite_time (mean (SD))        1.0   
  macmean0 (mean (SD))                 2.3   
  smcmean0 (mean (SD))                 2.7   
  Macrophages.bin % (freq)             1.9   
                                             
                                             
  SMC.bin % (freq)                     1.8   
                                             
                                             
  neutrophils (mean (SD))             82.0   
  Mast_cells_plaque (mean (SD))       86.2   
  IPH.bin % (freq)                     1.7   
                                             
                                             
  vessel_density_averaged (mean (SD))  8.8   
  Calc.bin % (freq)                    1.7   
                                             
                                             
  Collagen.bin % (freq)                1.6   
                                             
                                             
  Fat.bin_10 % (freq)                  1.6   
                                             
                                             
  Fat.bin_40 % (freq)                  1.6   
                                             
                                             
  OverallPlaquePhenotype % (freq)      1.8   
                                             
                                             
                                             
  IL6_pg_ug_2015 (mean (SD))           3.9   
  MCP1_pg_ug_2015 (mean (SD))          0.1   
AEDB.CEA.subset.serum <- subset(AEDB.CEA, !is.na(IL6) | !is.na(MCP1))

AEDB.CEA.subset.serum.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "DiabetesStatus",
                                         data = AEDB.CEA.subset.serum, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]
                                     
                                      level                                                                     Overall                
  n                                                                                                                    593             
  Hospital % (freq)                   St. Antonius, Nieuwegein                                                        64.6 (383)       
                                      UMC Utrecht                                                                     35.4 (210)       
  Age (mean (SD))                                                                                                   67.297 (8.787)     
  Gender % (freq)                     female                                                                          28.8 (171)       
                                      male                                                                            71.2 (422)       
  TC_final (mean (SD))                                                                                               4.958 (1.228)     
  LDL_final (mean (SD))                                                                                              2.935 (1.064)     
  HDL_final (mean (SD))                                                                                              1.195 (0.394)     
  TG_final (mean (SD))                                                                                               1.810 (0.932)     
  hsCRP_plasma (mean (SD))                                                                                          15.379 (80.024)    
  systolic (mean (SD))                                                                                             156.674 (26.187)    
  diastoli (mean (SD))                                                                                              83.496 (12.532)    
  GFR_MDRD (mean (SD))                                                                                              70.569 (19.052)    
  BMI (mean (SD))                                                                                                   26.528 (3.903)     
  KDOQI % (freq)                      No data available/missing                                                        0.0 (  0)       
                                      Normal kidney function                                                          14.8 ( 88)       
                                      CKD 2 (Mild)                                                                    56.2 (333)       
                                      CKD 3 (Moderate)                                                                25.6 (152)       
                                      CKD 4 (Severe)                                                                   1.0 (  6)       
                                      CKD 5 (Failure)                                                                  0.5 (  3)       
                                      <NA>                                                                             1.9 ( 11)       
  BMI_WHO % (freq)                    No data available/missing                                                        0.0 (  0)       
                                      Underweight                                                                      1.3 (  8)       
                                      Normal                                                                          33.1 (196)       
                                      Overweight                                                                      45.2 (268)       
                                      Obese                                                                           13.5 ( 80)       
                                      <NA>                                                                             6.9 ( 41)       
  SmokerCurrent % (freq)              no data available/missing                                                        0.0 (  0)       
                                      no                                                                              62.2 (369)       
                                      yes                                                                             36.6 (217)       
                                      <NA>                                                                             1.2 (  7)       
  eCigarettes (mean (SD))                                                                                       172610.335 (143977.960)
  ePackYearsSmoking (mean (SD))                                                                                     23.645 (19.723)    
  DiabetesStatus % (freq)             Control (no Diabetes Dx/Med)                                                    78.6 (466)       
                                      Diabetes                                                                        21.4 (127)       
  Hypertension.composite % (freq)     No data available/missing                                                        0.0 (  0)       
                                      no                                                                              13.2 ( 78)       
                                      yes                                                                             86.8 (515)       
  Hypertension.drugs % (freq)         No data available/missing                                                        0.0 (  0)       
                                      no                                                                              20.7 (123)       
                                      yes                                                                             78.9 (468)       
                                      <NA>                                                                             0.3 (  2)       
  Med.anticoagulants % (freq)         No data available/missing                                                        0.0 (  0)       
                                      no                                                                              86.8 (515)       
                                      yes                                                                             12.8 ( 76)       
                                      <NA>                                                                             0.3 (  2)       
  Med.all.antiplatelet % (freq)       No data available/missing                                                        0.0 (  0)       
                                      no                                                                               9.8 ( 58)       
                                      yes                                                                             89.9 (533)       
                                      <NA>                                                                             0.3 (  2)       
  Med.Statin.LLD % (freq)             No data available/missing                                                        0.0 (  0)       
                                      no                                                                              27.2 (161)       
                                      yes                                                                             72.5 (430)       
                                      <NA>                                                                             0.3 (  2)       
  Stroke_Dx % (freq)                  Missing                                                                          0.0 (  0)       
                                      No stroke diagnosed                                                             76.6 (454)       
                                      Stroke diagnosed                                                                17.0 (101)       
                                      <NA>                                                                             6.4 ( 38)       
  sympt % (freq)                      missing                                                                          0.0 (  0)       
                                      Asymptomatic                                                                    15.7 ( 93)       
                                      TIA                                                                             41.1 (244)       
                                      minor stroke                                                                    17.0 (101)       
                                      Major stroke                                                                     6.7 ( 40)       
                                      Amaurosis fugax                                                                 13.5 ( 80)       
                                      Four vessel disease                                                              2.2 ( 13)       
                                      Vertebrobasilary TIA                                                             0.2 (  1)       
                                      Retinal infarction                                                               0.3 (  2)       
                                      Symptomatic, but aspecific symtoms                                               3.0 ( 18)       
                                      Contralateral symptomatic occlusion                                              0.0 (  0)       
                                      retinal infarction                                                               0.0 (  0)       
                                      armclaudication due to occlusion subclavian artery, CEA needed for bypass        0.0 (  0)       
                                      retinal infarction + TIAs                                                        0.0 (  0)       
                                      Ocular ischemic syndrome                                                         0.0 (  0)       
                                      ischemisch glaucoom                                                              0.0 (  0)       
                                      subclavian steal syndrome                                                        0.0 (  0)       
                                      TGA                                                                              0.0 (  0)       
                                      <NA>                                                                             0.2 (  1)       
  Symptoms.5G % (freq)                Asymptomatic                                                                    15.7 ( 93)       
                                      Ocular                                                                          13.5 ( 80)       
                                      Other                                                                            5.2 ( 31)       
                                      Retinal infarction                                                               0.3 (  2)       
                                      Stroke                                                                          23.8 (141)       
                                      TIA                                                                             41.3 (245)       
                                      <NA>                                                                             0.2 (  1)       
  AsymptSympt % (freq)                Asymptomatic                                                                    15.7 ( 93)       
                                      Ocular and others                                                               19.1 (113)       
                                      Symptomatic                                                                     65.1 (386)       
                                      <NA>                                                                             0.2 (  1)       
  restenos % (freq)                   missing                                                                          0.0 (  0)       
                                      de novo                                                                         97.0 (575)       
                                      restenosis                                                                       3.0 ( 18)       
                                      stenose bij angioseal na PTCA                                                    0.0 (  0)       
  stenose % (freq)                    missing                                                                          0.0 (  0)       
                                      0-49%                                                                            0.5 (  3)       
                                      50-70%                                                                           3.4 ( 20)       
                                      70-90%                                                                          39.8 (236)       
                                      90-99%                                                                          54.8 (325)       
                                      100% (Occlusion)                                                                 1.3 (  8)       
                                      NA                                                                               0.0 (  0)       
                                      50-99%                                                                           0.0 (  0)       
                                      70-99%                                                                           0.0 (  0)       
                                      99                                                                               0.0 (  0)       
                                      <NA>                                                                             0.2 (  1)       
  EP_composite % (freq)               No data available.                                                               0.0 (  0)       
                                      No composite endpoints                                                          71.0 (421)       
                                      Composite endpoints                                                             28.0 (166)       
                                      <NA>                                                                             1.0 (  6)       
  EP_composite_time (mean (SD))                                                                                      2.616 (1.148)     
  macmean0 (mean (SD))                                                                                               0.962 (1.399)     
  smcmean0 (mean (SD))                                                                                               2.216 (2.485)     
  Macrophages.bin % (freq)            no/minor                                                                        41.8 (248)       
                                      moderate/heavy                                                                  57.3 (340)       
                                      <NA>                                                                             0.8 (  5)       
  SMC.bin % (freq)                    no/minor                                                                        29.5 (175)       
                                      moderate/heavy                                                                  69.8 (414)       
                                      <NA>                                                                             0.7 (  4)       
  neutrophils (mean (SD))                                                                                          109.084 (261.117)   
  Mast_cells_plaque (mean (SD))                                                                                    159.436 (156.529)   
  IPH.bin % (freq)                    no                                                                              23.9 (142)       
                                      yes                                                                             75.9 (450)       
                                      <NA>                                                                             0.2 (  1)       
  vessel_density_averaged (mean (SD))                                                                                9.001 (5.629)     
  Calc.bin % (freq)                   no/minor                                                                        41.1 (244)       
                                      moderate/heavy                                                                  58.7 (348)       
                                      <NA>                                                                             0.2 (  1)       
  Collagen.bin % (freq)               no/minor                                                                        18.5 (110)       
                                      moderate/heavy                                                                  80.9 (480)       
                                      <NA>                                                                             0.5 (  3)       
  Fat.bin_10 % (freq)                  <10%                                                                           19.6 (116)       
                                       >10%                                                                           80.3 (476)       
                                      <NA>                                                                             0.2 (  1)       
  Fat.bin_40 % (freq)                 <40%                                                                            66.1 (392)       
                                      >40%                                                                            33.7 (200)       
                                      <NA>                                                                             0.2 (  1)       
  OverallPlaquePhenotype % (freq)     atheromatous                                                                    33.7 (200)       
                                      fibroatheromatous                                                               36.6 (217)       
                                      fibrous                                                                         29.5 (175)       
                                      <NA>                                                                             0.2 (  1)       
  IL6_pg_ug_2015 (mean (SD))                                                                                         0.177 (0.809)     
  MCP1_pg_ug_2015 (mean (SD))                                                                                        0.561 (1.083)     
                                     
                                      Missing
  n                                          
  Hospital % (freq)                    0.0   
                                             
  Age (mean (SD))                      0.0   
  Gender % (freq)                      0.0   
                                             
  TC_final (mean (SD))                19.2   
  LDL_final (mean (SD))               29.8   
  HDL_final (mean (SD))               24.8   
  TG_final (mean (SD))                23.3   
  hsCRP_plasma (mean (SD))            45.0   
  systolic (mean (SD))                 6.2   
  diastoli (mean (SD))                 6.2   
  GFR_MDRD (mean (SD))                 1.7   
  BMI (mean (SD))                      6.7   
  KDOQI % (freq)                       1.9   
                                             
                                             
                                             
                                             
                                             
                                             
  BMI_WHO % (freq)                     6.9   
                                             
                                             
                                             
                                             
                                             
  SmokerCurrent % (freq)               1.2   
                                             
                                             
                                             
  eCigarettes (mean (SD))              9.4   
  ePackYearsSmoking (mean (SD))        9.4   
  DiabetesStatus % (freq)              0.0   
                                             
  Hypertension.composite % (freq)      0.0   
                                             
                                             
  Hypertension.drugs % (freq)          0.3   
                                             
                                             
                                             
  Med.anticoagulants % (freq)          0.3   
                                             
                                             
                                             
  Med.all.antiplatelet % (freq)        0.3   
                                             
                                             
                                             
  Med.Statin.LLD % (freq)              0.3   
                                             
                                             
                                             
  Stroke_Dx % (freq)                   6.4   
                                             
                                             
                                             
  sympt % (freq)                       0.2   
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
  Symptoms.5G % (freq)                 0.2   
                                             
                                             
                                             
                                             
                                             
                                             
  AsymptSympt % (freq)                 0.2   
                                             
                                             
                                             
  restenos % (freq)                    0.0   
                                             
                                             
                                             
  stenose % (freq)                     0.2   
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
  EP_composite % (freq)                1.0   
                                             
                                             
                                             
  EP_composite_time (mean (SD))        1.0   
  macmean0 (mean (SD))                 0.3   
  smcmean0 (mean (SD))                 1.0   
  Macrophages.bin % (freq)             0.8   
                                             
                                             
  SMC.bin % (freq)                     0.7   
                                             
                                             
  neutrophils (mean (SD))             77.9   
  Mast_cells_plaque (mean (SD))       72.2   
  IPH.bin % (freq)                     0.2   
                                             
                                             
  vessel_density_averaged (mean (SD))  3.0   
  Calc.bin % (freq)                    0.2   
                                             
                                             
  Collagen.bin % (freq)                0.5   
                                             
                                             
  Fat.bin_10 % (freq)                  0.2   
                                             
                                             
  Fat.bin_40 % (freq)                  0.2   
                                             
                                             
  OverallPlaquePhenotype % (freq)      0.2   
                                             
                                             
                                             
  IL6_pg_ug_2015 (mean (SD))          24.6   
  MCP1_pg_ug_2015 (mean (SD))         23.9   

Writing the baseline table to Excel format.

# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
           AEDB.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
           AEDB.CEA.subset.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "subsetAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAserum.xlsx"),
           AEDB.CEA.subset.serum.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "subsetAEDB_Baseline_serum")

Athero-Express Genomics Study (AEGS)

We will add the samples which were genotyped in the Athero-Express Biobank Study, i.e. Athero-Express Genomics Study 1 (AEGS1, Affymetrix SNP 5.0), AEGS2 (Affymetrix Axiom CEU), and AEGS3 (Illumina GSA).

Loading AEGS data

AEGS1_2_3 <- fread(paste0(ROOT_loc,"/PLINK/_AE_Originals/AEGS_COMBINED_QC2018/aegs1_2_3_combo_postqcmichimp_n2493.forR.txt"),
                   verbose = TRUE, showProgress = TRUE)
This installation of data.table has not been compiled with OpenMP support.
  omp_get_num_procs()            1
  R_DATATABLE_NUM_PROCS_PERCENT  unset (default 50)
  R_DATATABLE_NUM_THREADS        unset
  omp_get_thread_limit()         1
  omp_get_max_threads()          1
  OMP_THREAD_LIMIT               unset
  OMP_NUM_THREADS                unset
  RestoreAfterFork               true
  data.table is using 1 threads. See ?setDTthreads.
Input contains no \n. Taking this to be a filename to open
[01] Check arguments
  Using 1 threads (omp_get_max_threads()=1, nth=1)
  NAstrings = [<<NA>>]
  None of the NAstrings look like numbers.
  show progress = 1
  0/1 column will be read as integer
[02] Opening the file
  Opening file /Users/swvanderlaan/PLINK/_AE_Originals/AEGS_COMBINED_QC2018/aegs1_2_3_combo_postqcmichimp_n2493.forR.txt
  File opened, size = 315.1KB (322657 bytes).
  Memory mapped ok
[03] Detect and skip BOM
[04] Arrange mmap to be \0 terminated
  \n has been found in the input and different lines can end with different line endings (e.g. mixed \n and \r\n in one file). This is common and ideal.
  File ends abruptly with '4'. Final end-of-line is missing. Using cow page to write 0 to the last byte.
[05] Skipping initial rows if needed
  Positioned on line 1 starting: <<FID_forQC    IID_forQC   SampleID_p>>
[06] Detect separator, quoting rule, and ncolumns
  Detecting sep automatically ...
  sep=0x9  with 100 lines of 14 fields using quote rule 0
  Detected 14 columns on line 1. This line is either column names or first data row. Line starts as: <<FID_forQC    IID_forQC   SampleID_p>>
  Quote rule picked = 0
  fill=false and the most number of columns found is 14
[07] Detect column types, good nrow estimate and whether first row is column names
  Number of sampling jump points = 10 because (322657 bytes from row 1 to eof) / (2 * 15634 jump0size) == 10
  Type codes (jump 000)    : AAAA55AAAAAAA5  Quote rule 0
  Type codes (jump 010)    : AAAAA5AAAAAAA5  Quote rule 0
  'header' determined to be true due to column 6 containing a string on row 1 and a lower type (int32) in the rest of the 1070 sample rows
  =====
  Sampled 1070 rows (handled \n inside quoted fields) at 11 jump points
  Bytes from first data row on line 2 to the end of last row: 322506
  Line length: mean=132.61 sd=28.26 min=97 max=236
  Estimated number of rows: 322506 / 132.61 = 2432
  Initial alloc = 3324 rows (2432 + 36%) using bytes/max(mean-2*sd,min) clamped between [1.1*estn, 2.0*estn]
  =====
[08] Assign column names
[09] Apply user overrides on column types
  After 0 type and 0 drop user overrides : AAAAA5AAAAAAA5
[10] Allocate memory for the datatable
  Allocating 14 column slots (14 - 0 dropped) with 3324 rows
[11] Read the data
  jumps=[0..1), chunk_size=322506, total_size=322506
Read 2500 rows x 14 columns from 315.1KB (322657 bytes) file in 00:00.016 wall clock time
[12] Finalizing the datatable
  Type counts:
         2 : int32     '5'
        12 : string    'A'
=============================
   0.002s ( 11%) Memory map 0.000GB file
   0.007s ( 44%) sep='\t' ncol=14 and header detection
   0.000s (  2%) Column type detection using 1070 sample rows
   0.000s (  2%) Allocation of 3324 rows x 14 cols (0.000GB) of which 2500 ( 75%) rows used
   0.007s ( 41%) Reading 1 chunks (0 swept) of 0.308MB (each chunk 2500 rows) using 1 threads
   +    0.002s ( 14%) Parse to row-major thread buffers (grown 0 times)
   +    0.004s ( 25%) Transpose
   +    0.000s (  2%) Waiting
   0.000s (  0%) Rereading 0 columns due to out-of-sample type exceptions
   0.016s        Total
AEGS1_2_3$Study_Number <- as.numeric(AEGS1_2_3$Study_Number)
NAs introduced by coercion
AEGS1_2_3$Age <- NULL
dim(AEGS1_2_3)
[1] 2500   13
head(AEGS1_2_3)

AEGS_raw <- merge(AEGS1_2_3, AEDB, by.x = "Study_Number", by.y = "STUDY_NUMBER", sort = FALSE,
                  all.x = TRUE)

dim(AEGS_raw)
[1] 2500 1043
warnings() 

Here we will subset only those genotyped samples that passed genotyping quality control, are unrelated, and have informed consent.

AEGS_raw$Artery_summary <- to_factor(AEGS_raw$Artery_summary)
AEGS_raw$informedconsent <- to_factor(AEGS_raw$informedconsent)
table(AEGS_raw$Artery_summary, AEGS_raw$QC2018_FILTER)
                                                                                         
                                                                                          family_discard family_keep issue passed
  No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA              0           0     0      0
  carotid (left & right)                                                                              22          21   326   1957
  femoral/iliac (left, right or both sides)                                                            1           0    17    109
  other carotid arteries (common, external)                                                            0           0     0     11
  carotid bypass and injury (left, right or both sides)                                                0           0     0      1
  aneurysmata (carotid & femoral)                                                                      0           0     0      0
  aorta                                                                                                0           0     0      0
  other arteries (renal, popliteal, vertebral)                                                         0           0     0      1
  femoral bypass, angioseal and injury (left, right or both sides)                                     0           0     0      0
table(AEGS_raw$informedconsent, AEGS_raw$QC2018_FILTER)
                                                                                                 
                                                                                                  family_discard family_keep issue passed
  missing                                                                                                      0           0     0      0
  no, died                                                                                                     1           0     5     38
  yes                                                                                                         17          16   262   1435
  yes, health treatment when possible                                                                          2           2    41    322
  yes, no health treatment                                                                                     2           2     8     93
  yes, no health treatment, no commercial business                                                             0           0     0     15
  yes, no tissue, no commerical business                                                                       0           0     0      0
  yes, no tissue, no questionnaires, no medical info, no commercial business                                   0           0     0      0
  yes, no questionnaires, no health treatment, no commercial business                                          0           0     0      1
  yes, no questionnaires, health treatment when possible                                                       0           0     1      2
  yes, no tissue, no questionnaires, no health treatment, no commerical business                               0           0     0      0
  yes, no health treatment, no medical info, no commercial business                                            0           0     4     13
  yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business              0           0     0      0
  yes, no questionnaires, no health treatment                                                                  0           0     0      0
  yes, no tissue, no health treatment                                                                          0           0     0      0
  yes, no tissue, no questionnaires                                                                            0           0     0      0
  yes, no tissue, health treatment when possible                                                               0           0     0      0
  yes, no tissue                                                                                               0           0     0      0
  yes, no commerical business                                                                                  0           1     7     35
  yes, health treatment when possible, no commercial business                                                  0           0     0     25
  yes, no medical info, no commercial business                                                                 0           0     1      4
  yes, no questionnaires                                                                                       0           0     0      1
  yes, no tissue, no questionnaires, no health treatment, no medical info                                      0           0     0      0
  yes, no tissue, no questionnaires, no health treatment, no commercial business                               0           0     0      0
  yes, no medical info                                                                                         0           0     1      6
  yes, no questionnaires, no commercial business                                                               0           0     0      0
  yes, no questionnaires, no health treatment, no medical info                                                 0           0     0      1
  yes, no questionnaires, health treatment when possible, no commercial business                               0           0     0      0
  yes,  no health treatment, no medical info                                                                   0           0     0      5
  no, doesn't want to                                                                                          0           0     0      0
  no, unable to sign                                                                                           0           0     1     17
  no, no reaction                                                                                              0           0     6     14
  no, lost                                                                                                     0           0     2      6
  no, too old                                                                                                  0           0     4     18
  yes, no medical info, health treatment when possible                                                         0           0     0      2
  no (never asked for IC because there was no tissue)                                                          0           0     0      0
  yes, no medical info, no commercial business, health treatment when possible                                 0           0     0      2
  no, endpoint                                                                                                 0           0     0      0
  wil niets invullen, wel alles gebruiken                                                                      0           0     0      7
  second informed concents: yes, no commercial business                                                        0           0     0      2
  nooit geincludeerd                                                                                           0           0     0      0
AEGSselect <- subset(AEGS_raw, 
                     QC2018_FILTER != "issue" & QC2018_FILTER != "family_discard" &
                       (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & 
                       informedconsent != "missing" &
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd")
dim(AEGSselect)
[1] 1889 1043
table(AEGSselect$Artery_summary, AEGSselect$QC2018_FILTER)
                                                                                         
                                                                                          family_keep passed
  No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA           0      0
  carotid (left & right)                                                                           21   1859
  femoral/iliac (left, right or both sides)                                                         0      0
  other carotid arteries (common, external)                                                         0      9
  carotid bypass and injury (left, right or both sides)                                             0      0
  aneurysmata (carotid & femoral)                                                                   0      0
  aorta                                                                                             0      0
  other arteries (renal, popliteal, vertebral)                                                      0      0
  femoral bypass, angioseal and injury (left, right or both sides)                                  0      0
table(AEGSselect$Artery_summary, AEGSselect$CHIP)
                                                                                         
                                                                                          AffyAxiomCEU AffySNP5 IllGSA
  No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA            0        0      0
  carotid (left & right)                                                                           815      544    521
  femoral/iliac (left, right or both sides)                                                          0        0      0
  other carotid arteries (common, external)                                                          2        5      2
  carotid bypass and injury (left, right or both sides)                                              0        0      0
  aneurysmata (carotid & femoral)                                                                    0        0      0
  aorta                                                                                              0        0      0
  other arteries (renal, popliteal, vertebral)                                                       0        0      0
  femoral bypass, angioseal and injury (left, right or both sides)                                   0        0      0
table(AEGSselect$QC2018_FILTER, AEGSselect$CHIP)
             
              AffyAxiomCEU AffySNP5 IllGSA
  family_keep            8        1     12
  passed               809      548    511
table(AEGSselect$QC2018_FILTER, AEGSselect$SAMPLE_TYPE)
             
              EDTA blood plaque unknown
  family_keep         13      8       0
  passed            1174    693       1
AEDB.temp <- subset(AEGSselect,  select = c("Study_Number", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "QC2018_FILTER", "CHIP", "SAMPLE_TYPE"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
AEDB.temp$QC2018_FILTER <- to_factor(AEDB.temp$QC2018_FILTER)
AEDB.temp$CHIP <- to_factor(AEDB.temp$CHIP)
AEDB.temp$SAMPLE_TYPE <- to_factor(AEDB.temp$SAMPLE_TYPE)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)


rm(AEDB.temp)

Baseline characteristics

Showing the baseline table of the whole Athero-Express Genomics Study.

# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html

basetable_vars_geno = c("Hospital", 
                   "Age", "Gender", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
                   "DiabetesStatus", "Hypertension.composite", 
                   "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt",
                   "restenos", "stenose",
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
                   "QC2018_FILTER", "CHIP", "SAMPLE_TYPE")

basetable_bin_geno = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerCurrent", 
                  "DiabetesStatus", "Hypertension.composite", 
                  "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt",
                  "restenos", "stenose",
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                  "QC2018_FILTER", "CHIP", "SAMPLE_TYPE")

basetable_con_geno = basetable_vars_geno[!basetable_vars_geno %in% basetable_bin_geno]

AEGSselect.tableOne = print(CreateTableOne(vars = basetable_vars_geno, 
                                         # factorVars = basetable_bin,
                                         # strata = "DiabetesStatus",
                                         data = AEGSselect, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]
                                     
                                      level                                                                     Overall                
  n                                                                                                                   1889             
  Hospital % (freq)                   St. Antonius, Nieuwegein                                                        42.7 ( 807)      
                                      UMC Utrecht                                                                     57.3 (1082)      
  Age (mean (SD))                                                                                                   69.143 (9.186)     
  Gender % (freq)                     female                                                                          31.8 ( 600)      
                                      male                                                                            68.2 (1289)      
  TC_final (mean (SD))                                                                                               4.764 (1.246)     
  LDL_final (mean (SD))                                                                                              2.802 (1.069)     
  HDL_final (mean (SD))                                                                                              1.203 (0.461)     
  TG_final (mean (SD))                                                                                               1.678 (0.994)     
  hsCRP_plasma (mean (SD))                                                                                          21.657 (243.628)   
  systolic (mean (SD))                                                                                             153.227 (25.351)    
  diastoli (mean (SD))                                                                                              81.799 (27.599)    
  GFR_MDRD (mean (SD))                                                                                              72.702 (20.665)    
  BMI (mean (SD))                                                                                                   26.406 (3.890)     
  KDOQI % (freq)                      No data available/missing                                                        0.0 (   0)      
                                      Normal kidney function                                                          18.3 ( 346)      
                                      CKD 2 (Mild)                                                                    52.5 ( 992)      
                                      CKD 3 (Moderate)                                                                22.8 ( 430)      
                                      CKD 4 (Severe)                                                                   1.3 (  24)      
                                      CKD 5 (Failure)                                                                  0.4 (   7)      
                                      <NA>                                                                             4.8 (  90)      
  BMI_WHO % (freq)                    No data available/missing                                                        0.0 (   0)      
                                      Underweight                                                                      1.0 (  18)      
                                      Normal                                                                          35.8 ( 677)      
                                      Overweight                                                                      43.8 ( 827)      
                                      Obese                                                                           14.5 ( 273)      
                                      <NA>                                                                             5.0 (  94)      
  SmokerCurrent % (freq)              no data available/missing                                                        0.0 (   0)      
                                      no                                                                              65.7 (1242)      
                                      yes                                                                             33.1 ( 626)      
                                      <NA>                                                                             1.1 (  21)      
  eCigarettes (mean (SD))                                                                                       168694.092 (149902.259)
  ePackYearsSmoking (mean (SD))                                                                                     23.109 (20.535)    
  DiabetesStatus % (freq)             Control (no Diabetes Dx/Med)                                                    76.4 (1443)      
                                      Diabetes                                                                        23.6 ( 446)      
  Hypertension.composite % (freq)     No data available/missing                                                        0.0 (   0)      
                                      no                                                                              14.1 ( 267)      
                                      yes                                                                             85.9 (1622)      
  Hypertension.drugs % (freq)         No data available/missing                                                        0.0 (   0)      
                                      no                                                                              22.9 ( 433)      
                                      yes                                                                             77.0 (1454)      
                                      <NA>                                                                             0.1 (   2)      
  Med.anticoagulants % (freq)         No data available/missing                                                        0.0 (   0)      
                                      no                                                                              88.1 (1664)      
                                      yes                                                                             11.8 ( 223)      
                                      <NA>                                                                             0.1 (   2)      
  Med.all.antiplatelet % (freq)       No data available/missing                                                        0.0 (   0)      
                                      no                                                                              11.8 ( 222)      
                                      yes                                                                             88.0 (1663)      
                                      <NA>                                                                             0.2 (   4)      
  Med.Statin.LLD % (freq)             No data available/missing                                                        0.0 (   0)      
                                      no                                                                              20.6 ( 389)      
                                      yes                                                                             79.3 (1498)      
                                      <NA>                                                                             0.1 (   2)      
  Stroke_Dx % (freq)                  Missing                                                                          0.0 (   0)      
                                      No stroke diagnosed                                                             72.3 (1366)      
                                      Stroke diagnosed                                                                21.5 ( 407)      
                                      <NA>                                                                             6.1 ( 116)      
  sympt % (freq)                      missing                                                                          0.0 (   0)      
                                      Asymptomatic                                                                    10.5 ( 199)      
                                      TIA                                                                             40.9 ( 773)      
                                      minor stroke                                                                    17.2 ( 324)      
                                      Major stroke                                                                    10.0 ( 189)      
                                      Amaurosis fugax                                                                 14.2 ( 268)      
                                      Four vessel disease                                                              1.7 (  32)      
                                      Vertebrobasilary TIA                                                             0.2 (   4)      
                                      Retinal infarction                                                               1.5 (  29)      
                                      Symptomatic, but aspecific symtoms                                               2.3 (  44)      
                                      Contralateral symptomatic occlusion                                              0.3 (   6)      
                                      retinal infarction                                                               0.3 (   5)      
                                      armclaudication due to occlusion subclavian artery, CEA needed for bypass        0.1 (   1)      
                                      retinal infarction + TIAs                                                        0.1 (   2)      
                                      Ocular ischemic syndrome                                                         0.2 (   4)      
                                      ischemisch glaucoom                                                              0.0 (   0)      
                                      subclavian steal syndrome                                                        0.1 (   1)      
                                      TGA                                                                              0.0 (   0)      
                                      <NA>                                                                             0.4 (   8)      
  Symptoms.5G % (freq)                Asymptomatic                                                                    10.5 ( 199)      
                                      Ocular                                                                          14.4 ( 272)      
                                      Other                                                                            4.4 (  84)      
                                      Retinal infarction                                                               1.8 (  34)      
                                      Stroke                                                                          27.2 ( 513)      
                                      TIA                                                                             41.2 ( 779)      
                                      <NA>                                                                             0.4 (   8)      
  AsymptSympt % (freq)                Asymptomatic                                                                    10.5 ( 199)      
                                      Ocular and others                                                               20.6 ( 390)      
                                      Symptomatic                                                                     68.4 (1292)      
                                      <NA>                                                                             0.4 (   8)      
  restenos % (freq)                   missing                                                                          0.0 (   0)      
                                      de novo                                                                         95.0 (1795)      
                                      restenosis                                                                       3.4 (  65)      
                                      stenose bij angioseal na PTCA                                                    0.0 (   0)      
                                      <NA>                                                                             1.5 (  29)      
  stenose % (freq)                    missing                                                                          0.0 (   0)      
                                      0-49%                                                                            0.6 (  11)      
                                      50-70%                                                                           7.0 ( 133)      
                                      70-90%                                                                          46.9 ( 885)      
                                      90-99%                                                                          39.9 ( 754)      
                                      100% (Occlusion)                                                                 1.0 (  19)      
                                      NA                                                                               0.0 (   0)      
                                      50-99%                                                                           0.4 (   7)      
                                      70-99%                                                                           2.0 (  38)      
                                      99                                                                               0.0 (   0)      
                                      <NA>                                                                             2.2 (  42)      
  EP_composite % (freq)               No data available.                                                               0.0 (   0)      
                                      No composite endpoints                                                          73.0 (1379)      
                                      Composite endpoints                                                             24.7 ( 467)      
                                      <NA>                                                                             2.3 (  43)      
  EP_composite_time (mean (SD))                                                                                      2.533 (1.091)     
  macmean0 (mean (SD))                                                                                               0.806 (1.235)     
  smcmean0 (mean (SD))                                                                                               1.983 (2.314)     
  Macrophages.bin % (freq)            no/minor                                                                        37.0 ( 699)      
                                      moderate/heavy                                                                  45.5 ( 860)      
                                      <NA>                                                                            17.5 ( 330)      
  SMC.bin % (freq)                    no/minor                                                                        26.4 ( 498)      
                                      moderate/heavy                                                                  56.3 (1064)      
                                      <NA>                                                                            17.3 ( 327)      
  neutrophils (mean (SD))                                                                                          136.736 (276.117)   
  Mast_cells_plaque (mean (SD))                                                                                    161.412 (164.100)   
  IPH.bin % (freq)                    no                                                                              32.9 ( 622)      
                                      yes                                                                             50.0 ( 945)      
                                      <NA>                                                                            17.0 ( 322)      
  vessel_density_averaged (mean (SD))                                                                                8.407 (6.362)     
  Calc.bin % (freq)                   no/minor                                                                        44.4 ( 838)      
                                      moderate/heavy                                                                  38.6 ( 730)      
                                      <NA>                                                                            17.0 ( 321)      
  Collagen.bin % (freq)               no/minor                                                                        17.4 ( 329)      
                                      moderate/heavy                                                                  65.3 (1234)      
                                      <NA>                                                                            17.3 ( 326)      
  Fat.bin_10 % (freq)                  <10%                                                                           22.8 ( 430)      
                                       >10%                                                                           60.3 (1140)      
                                      <NA>                                                                            16.9 ( 319)      
  Fat.bin_40 % (freq)                 <40%                                                                            60.1 (1135)      
                                      >40%                                                                            23.0 ( 435)      
                                      <NA>                                                                            16.9 ( 319)      
  OverallPlaquePhenotype % (freq)     atheromatous                                                                    22.3 ( 422)      
                                      fibroatheromatous                                                               30.5 ( 576)      
                                      fibrous                                                                         30.1 ( 569)      
                                      <NA>                                                                            17.0 ( 322)      
  IL6_pg_ug_2015 (mean (SD))                                                                                         0.140 (0.570)     
  MCP1_pg_ug_2015 (mean (SD))                                                                                        0.611 (0.913)     
  QC2018_FILTER % (freq)              family_keep                                                                      1.1 (  21)      
                                      passed                                                                          98.9 (1868)      
  CHIP % (freq)                       AffyAxiomCEU                                                                    43.3 ( 817)      
                                      AffySNP5                                                                        29.1 ( 549)      
                                      IllGSA                                                                          27.7 ( 523)      
  SAMPLE_TYPE % (freq)                EDTA blood                                                                      62.8 (1187)      
                                      plaque                                                                          37.1 ( 701)      
                                      unknown                                                                          0.1 (   1)      
                                     
                                      Missing
  n                                          
  Hospital % (freq)                    0.0   
                                             
  Age (mean (SD))                      0.0   
  Gender % (freq)                      0.0   
                                             
  TC_final (mean (SD))                36.8   
  LDL_final (mean (SD))               43.9   
  HDL_final (mean (SD))               40.3   
  TG_final (mean (SD))                41.2   
  hsCRP_plasma (mean (SD))            45.5   
  systolic (mean (SD))                11.8   
  diastoli (mean (SD))                11.8   
  GFR_MDRD (mean (SD))                 4.7   
  BMI (mean (SD))                      4.9   
  KDOQI % (freq)                       4.8   
                                             
                                             
                                             
                                             
                                             
                                             
  BMI_WHO % (freq)                     5.0   
                                             
                                             
                                             
                                             
                                             
  SmokerCurrent % (freq)               1.1   
                                             
                                             
                                             
  eCigarettes (mean (SD))             11.1   
  ePackYearsSmoking (mean (SD))       11.1   
  DiabetesStatus % (freq)              0.0   
                                             
  Hypertension.composite % (freq)      0.0   
                                             
                                             
  Hypertension.drugs % (freq)          0.1   
                                             
                                             
                                             
  Med.anticoagulants % (freq)          0.1   
                                             
                                             
                                             
  Med.all.antiplatelet % (freq)        0.2   
                                             
                                             
                                             
  Med.Statin.LLD % (freq)              0.1   
                                             
                                             
                                             
  Stroke_Dx % (freq)                   6.1   
                                             
                                             
                                             
  sympt % (freq)                       0.4   
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
  Symptoms.5G % (freq)                 0.4   
                                             
                                             
                                             
                                             
                                             
                                             
  AsymptSympt % (freq)                 0.4   
                                             
                                             
                                             
  restenos % (freq)                    1.5   
                                             
                                             
                                             
                                             
  stenose % (freq)                     2.2   
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
  EP_composite % (freq)                2.3   
                                             
                                             
                                             
  EP_composite_time (mean (SD))        2.4   
  macmean0 (mean (SD))                22.6   
  smcmean0 (mean (SD))                22.8   
  Macrophages.bin % (freq)            17.5   
                                             
                                             
  SMC.bin % (freq)                    17.3   
                                             
                                             
  neutrophils (mean (SD))             87.0   
  Mast_cells_plaque (mean (SD))       88.8   
  IPH.bin % (freq)                    17.0   
                                             
                                             
  vessel_density_averaged (mean (SD)) 27.9   
  Calc.bin % (freq)                   17.0   
                                             
                                             
  Collagen.bin % (freq)               17.3   
                                             
                                             
  Fat.bin_10 % (freq)                 16.9   
                                             
                                             
  Fat.bin_40 % (freq)                 16.9   
                                             
                                             
  OverallPlaquePhenotype % (freq)     17.0   
                                             
                                             
                                             
  IL6_pg_ug_2015 (mean (SD))          42.3   
  MCP1_pg_ug_2015 (mean (SD))         40.0   
  QC2018_FILTER % (freq)               0.0   
                                             
  CHIP % (freq)                        0.0   
                                             
                                             
  SAMPLE_TYPE % (freq)                 0.0   
                                             
                                             
AEGSselect.subset <- subset(AEGSselect, !is.na(IL6R_pg_ug_2015) | !is.na(MCP1_pg_ug_2015))

AEGSselect.subset.tableOne = print(CreateTableOne(vars = basetable_vars_geno, 
                                         # factorVars = basetable_bin,
                                         # strata = "DiabetesStatus",
                                         data = AEGSselect.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]
                                     
                                      level                                                                     Overall                
  n                                                                                                                   1135             
  Hospital % (freq)                   St. Antonius, Nieuwegein                                                        48.1 ( 546)      
                                      UMC Utrecht                                                                     51.9 ( 589)      
  Age (mean (SD))                                                                                                   68.793 (9.141)     
  Gender % (freq)                     female                                                                          31.2 ( 354)      
                                      male                                                                            68.8 ( 781)      
  TC_final (mean (SD))                                                                                               4.716 (1.240)     
  LDL_final (mean (SD))                                                                                              2.794 (1.042)     
  HDL_final (mean (SD))                                                                                              1.181 (0.472)     
  TG_final (mean (SD))                                                                                               1.655 (0.966)     
  hsCRP_plasma (mean (SD))                                                                                          15.331 (108.739)   
  systolic (mean (SD))                                                                                             156.078 (25.938)    
  diastoli (mean (SD))                                                                                              82.773 (13.368)    
  GFR_MDRD (mean (SD))                                                                                              71.563 (19.944)    
  BMI (mean (SD))                                                                                                   26.344 (3.729)     
  KDOQI % (freq)                      No data available/missing                                                        0.0 (   0)      
                                      Normal kidney function                                                          16.7 ( 190)      
                                      CKD 2 (Mild)                                                                    53.8 ( 611)      
                                      CKD 3 (Moderate)                                                                24.7 ( 280)      
                                      CKD 4 (Severe)                                                                   1.1 (  13)      
                                      CKD 5 (Failure)                                                                  0.4 (   5)      
                                      <NA>                                                                             3.2 (  36)      
  BMI_WHO % (freq)                    No data available/missing                                                        0.0 (   0)      
                                      Underweight                                                                      1.0 (  11)      
                                      Normal                                                                          35.3 ( 401)      
                                      Overweight                                                                      46.2 ( 524)      
                                      Obese                                                                           13.0 ( 147)      
                                      <NA>                                                                             4.6 (  52)      
  SmokerCurrent % (freq)              no data available/missing                                                        0.0 (   0)      
                                      no                                                                              63.2 ( 717)      
                                      yes                                                                             35.2 ( 400)      
                                      <NA>                                                                             1.6 (  18)      
  eCigarettes (mean (SD))                                                                                       169723.220 (152110.961)
  ePackYearsSmoking (mean (SD))                                                                                     23.250 (20.837)    
  DiabetesStatus % (freq)             Control (no Diabetes Dx/Med)                                                    77.4 ( 879)      
                                      Diabetes                                                                        22.6 ( 256)      
  Hypertension.composite % (freq)     No data available/missing                                                        0.0 (   0)      
                                      no                                                                              13.7 ( 155)      
                                      yes                                                                             86.3 ( 980)      
  Hypertension.drugs % (freq)         No data available/missing                                                        0.0 (   0)      
                                      no                                                                              22.3 ( 253)      
                                      yes                                                                             77.5 ( 880)      
                                      <NA>                                                                             0.2 (   2)      
  Med.anticoagulants % (freq)         No data available/missing                                                        0.0 (   0)      
                                      no                                                                              87.7 ( 995)      
                                      yes                                                                             12.2 ( 138)      
                                      <NA>                                                                             0.2 (   2)      
  Med.all.antiplatelet % (freq)       No data available/missing                                                        0.0 (   0)      
                                      no                                                                              10.7 ( 121)      
                                      yes                                                                             89.0 (1010)      
                                      <NA>                                                                             0.4 (   4)      
  Med.Statin.LLD % (freq)             No data available/missing                                                        0.0 (   0)      
                                      no                                                                              21.8 ( 247)      
                                      yes                                                                             78.1 ( 886)      
                                      <NA>                                                                             0.2 (   2)      
  Stroke_Dx % (freq)                  Missing                                                                          0.0 (   0)      
                                      No stroke diagnosed                                                             75.9 ( 862)      
                                      Stroke diagnosed                                                                18.7 ( 212)      
                                      <NA>                                                                             5.4 (  61)      
  sympt % (freq)                      missing                                                                          0.0 (   0)      
                                      Asymptomatic                                                                    10.7 ( 122)      
                                      TIA                                                                             41.6 ( 472)      
                                      minor stroke                                                                    15.1 ( 171)      
                                      Major stroke                                                                    10.8 ( 123)      
                                      Amaurosis fugax                                                                 14.6 ( 166)      
                                      Four vessel disease                                                              1.9 (  22)      
                                      Vertebrobasilary TIA                                                             0.2 (   2)      
                                      Retinal infarction                                                               1.3 (  15)      
                                      Symptomatic, but aspecific symtoms                                               2.4 (  27)      
                                      Contralateral symptomatic occlusion                                              0.5 (   6)      
                                      retinal infarction                                                               0.3 (   3)      
                                      armclaudication due to occlusion subclavian artery, CEA needed for bypass        0.1 (   1)      
                                      retinal infarction + TIAs                                                        0.0 (   0)      
                                      Ocular ischemic syndrome                                                         0.1 (   1)      
                                      ischemisch glaucoom                                                              0.0 (   0)      
                                      subclavian steal syndrome                                                        0.0 (   0)      
                                      TGA                                                                              0.0 (   0)      
                                      <NA>                                                                             0.4 (   4)      
  Symptoms.5G % (freq)                Asymptomatic                                                                    10.7 ( 122)      
                                      Ocular                                                                          14.7 ( 167)      
                                      Other                                                                            4.9 (  56)      
                                      Retinal infarction                                                               1.6 (  18)      
                                      Stroke                                                                          25.9 ( 294)      
                                      TIA                                                                             41.8 ( 474)      
                                      <NA>                                                                             0.4 (   4)      
  AsymptSympt % (freq)                Asymptomatic                                                                    10.7 ( 122)      
                                      Ocular and others                                                               21.2 ( 241)      
                                      Symptomatic                                                                     67.7 ( 768)      
                                      <NA>                                                                             0.4 (   4)      
  restenos % (freq)                   missing                                                                          0.0 (   0)      
                                      de novo                                                                         94.8 (1076)      
                                      restenosis                                                                       2.8 (  32)      
                                      stenose bij angioseal na PTCA                                                    0.0 (   0)      
                                      <NA>                                                                             2.4 (  27)      
  stenose % (freq)                    missing                                                                          0.0 (   0)      
                                      0-49%                                                                            0.5 (   6)      
                                      50-70%                                                                           5.6 (  64)      
                                      70-90%                                                                          45.3 ( 514)      
                                      90-99%                                                                          43.3 ( 492)      
                                      100% (Occlusion)                                                                 0.9 (  10)      
                                      NA                                                                               0.0 (   0)      
                                      50-99%                                                                           0.3 (   3)      
                                      70-99%                                                                           0.9 (  10)      
                                      99                                                                               0.0 (   0)      
                                      <NA>                                                                             3.2 (  36)      
  EP_composite % (freq)               No data available.                                                               0.0 (   0)      
                                      No composite endpoints                                                          73.5 ( 834)      
                                      Composite endpoints                                                             25.6 ( 291)      
                                      <NA>                                                                             0.9 (  10)      
  EP_composite_time (mean (SD))                                                                                      2.624 (1.115)     
  macmean0 (mean (SD))                                                                                               0.811 (1.239)     
  smcmean0 (mean (SD))                                                                                               1.950 (2.206)     
  Macrophages.bin % (freq)            no/minor                                                                        46.7 ( 530)      
                                      moderate/heavy                                                                  51.8 ( 588)      
                                      <NA>                                                                             1.5 (  17)      
  SMC.bin % (freq)                    no/minor                                                                        31.3 ( 355)      
                                      moderate/heavy                                                                  67.3 ( 764)      
                                      <NA>                                                                             1.4 (  16)      
  neutrophils (mean (SD))                                                                                          143.096 (279.963)   
  Mast_cells_plaque (mean (SD))                                                                                    171.063 (174.262)   
  IPH.bin % (freq)                    no                                                                              38.0 ( 431)      
                                      yes                                                                             60.7 ( 689)      
                                      <NA>                                                                             1.3 (  15)      
  vessel_density_averaged (mean (SD))                                                                                8.521 (6.460)     
  Calc.bin % (freq)                   no/minor                                                                        50.2 ( 570)      
                                      moderate/heavy                                                                  48.5 ( 551)      
                                      <NA>                                                                             1.2 (  14)      
  Collagen.bin % (freq)               no/minor                                                                        20.7 ( 235)      
                                      moderate/heavy                                                                  78.2 ( 888)      
                                      <NA>                                                                             1.1 (  12)      
  Fat.bin_10 % (freq)                  <10%                                                                           26.3 ( 298)      
                                       >10%                                                                           72.6 ( 824)      
                                      <NA>                                                                             1.1 (  13)      
  Fat.bin_40 % (freq)                 <40%                                                                            71.3 ( 809)      
                                      >40%                                                                            27.6 ( 313)      
                                      <NA>                                                                             1.1 (  13)      
  OverallPlaquePhenotype % (freq)     atheromatous                                                                    27.4 ( 311)      
                                      fibroatheromatous                                                               36.2 ( 411)      
                                      fibrous                                                                         35.1 ( 398)      
                                      <NA>                                                                             1.3 (  15)      
  IL6_pg_ug_2015 (mean (SD))                                                                                         0.140 (0.570)     
  MCP1_pg_ug_2015 (mean (SD))                                                                                        0.611 (0.913)     
  QC2018_FILTER % (freq)              family_keep                                                                      0.4 (   5)      
                                      passed                                                                          99.6 (1130)      
  CHIP % (freq)                       AffyAxiomCEU                                                                    54.3 ( 616)      
                                      AffySNP5                                                                        40.5 ( 460)      
                                      IllGSA                                                                           5.2 (  59)      
  SAMPLE_TYPE % (freq)                EDTA blood                                                                      55.9 ( 635)      
                                      plaque                                                                          44.0 ( 499)      
                                      unknown                                                                          0.1 (   1)      
                                     
                                      Missing
  n                                          
  Hospital % (freq)                    0.0   
                                             
  Age (mean (SD))                      0.0   
  Gender % (freq)                      0.0   
                                             
  TC_final (mean (SD))                32.2   
  LDL_final (mean (SD))               38.7   
  HDL_final (mean (SD))               35.2   
  TG_final (mean (SD))                34.9   
  hsCRP_plasma (mean (SD))            38.0   
  systolic (mean (SD))                14.4   
  diastoli (mean (SD))                14.4   
  GFR_MDRD (mean (SD))                 3.1   
  BMI (mean (SD))                      4.4   
  KDOQI % (freq)                       3.2   
                                             
                                             
                                             
                                             
                                             
                                             
  BMI_WHO % (freq)                     4.6   
                                             
                                             
                                             
                                             
                                             
  SmokerCurrent % (freq)               1.6   
                                             
                                             
                                             
  eCigarettes (mean (SD))              9.7   
  ePackYearsSmoking (mean (SD))        9.7   
  DiabetesStatus % (freq)              0.0   
                                             
  Hypertension.composite % (freq)      0.0   
                                             
                                             
  Hypertension.drugs % (freq)          0.2   
                                             
                                             
                                             
  Med.anticoagulants % (freq)          0.2   
                                             
                                             
                                             
  Med.all.antiplatelet % (freq)        0.4   
                                             
                                             
                                             
  Med.Statin.LLD % (freq)              0.2   
                                             
                                             
                                             
  Stroke_Dx % (freq)                   5.4   
                                             
                                             
                                             
  sympt % (freq)                       0.4   
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
  Symptoms.5G % (freq)                 0.4   
                                             
                                             
                                             
                                             
                                             
                                             
  AsymptSympt % (freq)                 0.4   
                                             
                                             
                                             
  restenos % (freq)                    2.4   
                                             
                                             
                                             
                                             
  stenose % (freq)                     3.2   
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
                                             
  EP_composite % (freq)                0.9   
                                             
                                             
                                             
  EP_composite_time (mean (SD))        1.1   
  macmean0 (mean (SD))                 1.8   
  smcmean0 (mean (SD))                 2.1   
  Macrophages.bin % (freq)             1.5   
                                             
                                             
  SMC.bin % (freq)                     1.4   
                                             
                                             
  neutrophils (mean (SD))             81.7   
  Mast_cells_plaque (mean (SD))       86.0   
  IPH.bin % (freq)                     1.3   
                                             
                                             
  vessel_density_averaged (mean (SD))  8.0   
  Calc.bin % (freq)                    1.2   
                                             
                                             
  Collagen.bin % (freq)                1.1   
                                             
                                             
  Fat.bin_10 % (freq)                  1.1   
                                             
                                             
  Fat.bin_40 % (freq)                  1.1   
                                             
                                             
  OverallPlaquePhenotype % (freq)      1.3   
                                             
                                             
                                             
  IL6_pg_ug_2015 (mean (SD))           4.0   
  MCP1_pg_ug_2015 (mean (SD))          0.1   
  QC2018_FILTER % (freq)               0.0   
                                             
  CHIP % (freq)                        0.0   
                                             
                                             
  SAMPLE_TYPE % (freq)                 0.0   
                                             
                                             

Let’s also save these baseline tables.

# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AEGS.BaselineTable.xlsx"), 
           AEGSselect.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "AEGS_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AEGS.BaselineTable.subset.xlsx"), 
           AEGSselect.subset.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "AEGS_Baseline_subset")

Data exploration

Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation.


summary(AEDB.CEA$IL6_pg_ug_2015)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0157  0.0391  0.1376  0.0985 13.1882    1234 
ggpubr::gghistogram(AEDB.CEA, "IL6_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

min_IL6pg_ug_2015 <- min(AEDB.CEA$IL6_pg_ug_2015, na.rm = TRUE)
min_IL6pg_ug_2015
[1] 1.927428e-05
AEDB.CEA$IL6_pg_ug_2015_LN <- log(AEDB.CEA$IL6_pg_ug_2015 + min_IL6pg_ug_2015)

ggpubr::gghistogram(AEDB.CEA, "IL6_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$IL6_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$IL6_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$IL6_pg_ug_2015)))
ggpubr::gghistogram(AEDB.CEA, "IL6_pg_ug_2015_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 plaque levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.


summary(AEDB.CEA$IL6R_pg_ug_2015)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0756  0.1472  0.2114  0.2526  4.0376    1232 
ggpubr::gghistogram(AEDB.CEA, "IL6R_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6R plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

min_IL6R_pg_ug_2015 <- min(AEDB.CEA$IL6R_pg_ug_2015, na.rm = TRUE)
min_IL6R_pg_ug_2015
[1] 0
AEDB.CEA$IL6R_pg_ug_2015_LN <- log(AEDB.CEA$IL6R_pg_ug_2015 + min_IL6R_pg_ug_2015)
ggpubr::gghistogram(AEDB.CEA, "IL6R_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6R plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$IL6R_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$IL6R_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$IL6R_pg_ug_2015)))
ggpubr::gghistogram(AEDB.CEA, "IL6R_pg_ug_2015_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6R plaque levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.


summary(AEDB.CEA$MCP1_pg_ug_2015)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0005  0.1376  0.3407  0.6120  0.7240 10.8540    1188 
ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015
[1] 0.0004584575
AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.


summary(AEDB.CEA$IL6)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   18.31   42.24   98.62   91.53 4586.07    1859 
ggpubr::gghistogram(AEDB.CEA, "IL6", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 serum levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

min_IL6 <- min(AEDB.CEA$IL6, na.rm = TRUE)
min_IL6
[1] 0
AEDB.CEA$IL6_LN <- log(AEDB.CEA$IL6 + min_IL6)
ggpubr::gghistogram(AEDB.CEA, "IL6_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 serum levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$IL6_rank <- qnorm((rank(AEDB.CEA$IL6, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$IL6)))
ggpubr::gghistogram(AEDB.CEA, "IL6_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 serum levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.


summary(AEDB.CEA$MCP1)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
   0.00   56.42  103.00  135.76  178.99  926.27    1823 
ggpubr::gghistogram(AEDB.CEA, "MCP1", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1
[1] 0
AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
ggpubr::gghistogram(AEDB.CEA, "MCP1_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
ggpubr::gghistogram(AEDB.CEA, "MCP1_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

Preliminary conclusion data exploration

After discussion we decided to pursuit the following strategy. In line with the previous work by Marios Georgakis we will apply natural log transformation on all proteins and focus the analysis on:

  • MCP1 (serum and plaque),
  • IL6 (serum and plaque), and
  • IL6R (in plaque).

Analyses

The analyses are focused on three elements:

  1. plaque vulnerability phenotypes
  2. clinical status at inclusion (symptoms)
  3. secondary clinical outcome during three (3) years of follow-up

Covariates & other variables

  1. Age (continuous in 1-year increment). [Age]
  2. Sex (male vs. female). [Gender]
  3. Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
  4. Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
  5. Smoking (current, ex-, never). [SmokerCurrent]
  6. LDL-C levels (continuous). [LDL_final]
  7. Use of lipid-lowering drugs. [Med.Statin.LLD]
  8. Use of antiplatelet drugs. [Med.all.antiplatelet]
  9. eGFR (continuous). [GFR_MDRD]
  10. BMI (continuous). [BMI]
  11. History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [CAD_history, Stroke_history, Peripheral.interv]
  12. Level of stenosis (50-70% vs. 70-99%). [stenose]
  13. Presenting symptoms (asymptomatic, ocular, TIA, or stroke). [Symptoms.5G]
  14. hsCRP circulating levels (ln-transformed, continuous). [hsCRP_plasma]
  15. IL-6 plaque levels (ln-transformed, continuous). [IL6_pg_ug_2015_LN]

Models

We will analyze the data through four different models

  • Model 1: adjusted for age and sex
  • Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
  • Model 3: same to model 2, with additional adjustments for circulating CRP levels
  • Model 4: same to model 2 with additional adjustment for IL6 levels in the plaque

A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque and serum MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:

  • Percentage of macrophages (continuous trait)
  • Percentage of SMCs (continuous trait)
  • Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
  • Presence of moderate/heavy calcifications (binary trait)
  • Presence of moderate/heavy collagen content (binary trait)
  • Presence of lipid core no/<10% vs. >10% (binary trait)
  • Presence of intraplaque hemorrhage (binary trait)

Continous traits


# macrophages
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$macmean0)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0733  0.3133  0.7688  1.0000 15.1000     679 
min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

Minimum value % macrophages: 0.
AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)

ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

# smooth muscle cells
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$macmean0)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
 0.0000  0.0733  0.3133  0.7688  1.0000 15.1000     679 
min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

Minimum value % smooth muscle cells: 0.
AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)

ggpubr::gghistogram(AEDB.CEA, "SMC_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

# vessel density
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$vessel_density_averaged)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  0.000   4.000   7.000   8.322  11.300  48.000     813 
min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity
[1] 0
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

Minimum value number of intraplaque neovessels per 3-4 hotspots: 0.
AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)

ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                    xlab = "natural log-transformed number", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
Using `bins = 30` by default. Pick better value with the argument `bins`.

Binary traits


# calcification
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$Calc.bin)
      no/minor moderate/heavy           NA's 
          1005            852            531 
contrasts(AEDB.CEA$Calc.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CalcificationPlaque)) %>%
  group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())

rm(df)

# collagen
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$Collagen.bin)
      no/minor moderate/heavy           NA's 
           381           1472            535 
contrasts(AEDB.CEA$Collagen.bin)
               moderate/heavy
no/minor                    0
moderate/heavy              1
AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CollagenPlaque)) %>%
  group_by(Gender, CollagenPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())

rm(df)

# fat 10%
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$Fat.bin_10)
 <10%  >10%  NA's 
  541  1321   526 
contrasts(AEDB.CEA$Fat.bin_10)
       >10%
 <10%     0
 >10%     1
AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)

df <- AEDB.CEA %>%
  filter(!is.na(Fat10Perc)) %>%
  group_by(Gender, Fat10Perc) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())

rm(df)

# IPH
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$IPH.bin)
  no  yes NA's 
 747 1111  530 
contrasts(AEDB.CEA$IPH.bin)
    yes
no    0
yes   1
AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)

df <- AEDB.CEA %>%
  filter(!is.na(IPH)) %>%
  group_by(Gender, IPH) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())

rm(df)

# Symptoms
cat("Summary of data.\n")
Summary of data.
summary(AEDB.CEA$AsymptSympt)
     Asymptomatic Ocular and others       Symptomatic              NA's 
              266               529              1582                11 
contrasts(AEDB.CEA$AsymptSympt)
                  Ocular and others Symptomatic
Asymptomatic                      0           0
Ocular and others                 1           0
Symptomatic                       0           1
AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)

df <- AEDB.CEA %>%
  filter(!is.na(AsymptSympt)) %>%
  group_by(Gender, AsymptSympt) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())

rm(df)

In this section we make some variables to assist with analysis.

AEDB.CEA.samplesize = nrow(AEDB.CEA)
TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")

TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN") 
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.   BMI (continuous). [BMI]
# 11.   History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [CAD_history, Stroke_history, Peripheral.interv]
# 12.   Level of stenosis (50-70% vs. 70-99%). [stenose]
# 13.   Presenting symptoms (asymptomatic, ocular, TIA, or stroke). [Symptoms.5G]
# 14.   hsCRP circulating levels (ln-transformed, continuous). [hsCRP_plasma]
# 15.   IL-6 plaque levels (ln-transformed, continuous). [IL6_pg_ug_2015_LN]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
# Model 3: same to model 2, with additional adjustments for circulating CRP levels
# Model 4: same to model 2 with additional adjustment for IL6 levels in the plaque

COVARIATES_M1 = c("Age", "Gender")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               "CAD_history", "Stroke_history", "Peripheral.interv",
               "stenose")

COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")

Model 1

In this model we correct for Age and Gender.

Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_LN.

- processing Macrophages_LN
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(PROTEIN)` instead of `PROTEIN` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(TRAIT)` instead of `TRAIT` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(COVARIATES_M1)` instead of `COVARIATES_M1` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.

Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)

Coefficients:
(Intercept)   Gendermale  
     4.1909      -0.2708  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9465 -0.7303 -0.0195  0.6615  4.2982 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)         4.1972956  0.4035182  10.402   <2e-16 ***
currentDF[, TRAIT] -0.0251343  0.0259780  -0.968   0.3338    
Age                -0.0006383  0.0058262  -0.110   0.9128    
Gendermale         -0.2645982  0.1132391  -2.337   0.0199 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.059 on 437 degrees of freedom
Multiple R-squared:  0.01508,   Adjusted R-squared:  0.008318 
F-statistic:  2.23 on 3 and 437 DF,  p-value: 0.08402

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: -0.025134 
Standard error............: 0.025978 
Odds ratio (effect size)..: 0.975 
Lower 95% CI..............: 0.927 
Upper 95% CI..............: 1.026 
T-value...................: -0.96752 
P-value...................: 0.3338192 
R^2.......................: 0.01508 
Adjusted r^2..............: 0.008318 
Sample size of AE DB......: 2388 
Sample size of model......: 441 
Missing data %............: 81.53266 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)

Coefficients:
(Intercept)   Gendermale  
     4.2091      -0.2669  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8987 -0.7242 -0.0527  0.6511  4.2160 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         4.314537   0.399343  10.804   <2e-16 ***
currentDF[, TRAIT]  0.007338   0.031360   0.234   0.8151    
Age                -0.001604   0.005778  -0.278   0.7815    
Gendermale         -0.263661   0.110671  -2.382   0.0176 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.045 on 442 degrees of freedom
Multiple R-squared:  0.01344,   Adjusted R-squared:  0.006746 
F-statistic: 2.008 on 3 and 442 DF,  p-value: 0.1122

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: 0.007338 
Standard error............: 0.03136 
Odds ratio (effect size)..: 1.007 
Lower 95% CI..............: 0.947 
Upper 95% CI..............: 1.071 
T-value...................: 0.233991 
P-value...................: 0.8151004 
R^2.......................: 0.013442 
Adjusted r^2..............: 0.006746 
Sample size of AE DB......: 2388 
Sample size of model......: 446 
Missing data %............: 81.32328 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)

Coefficients:
(Intercept)   Gendermale  
     4.1737      -0.2411  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9272 -0.7067 -0.0407  0.6631  4.2723 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         4.443868   0.434233  10.234   <2e-16 ***
currentDF[, TRAIT] -0.013742   0.076611  -0.179   0.8577    
Age                -0.003625   0.005870  -0.618   0.5372    
Gendermale         -0.238944   0.112701  -2.120   0.0346 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.062 on 435 degrees of freedom
Multiple R-squared:  0.01135,   Adjusted R-squared:  0.004533 
F-statistic: 1.665 on 3 and 435 DF,  p-value: 0.1739

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.013742 
Standard error............: 0.076611 
Odds ratio (effect size)..: 0.986 
Lower 95% CI..............: 0.849 
Upper 95% CI..............: 1.146 
T-value...................: -0.179371 
P-value...................: 0.8577301 
R^2.......................: 0.011351 
Adjusted r^2..............: 0.004533 
Sample size of AE DB......: 2388 
Sample size of model......: 439 
Missing data %............: 81.61642 

Analysis of MCP1_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age          Gendermale  
          4.923018            0.046963           -0.005936            0.195165  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3536 -0.5289  0.0671  0.5818  2.1581 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         4.923018   0.285874  17.221   <2e-16 ***
currentDF[, TRAIT]  0.046963   0.018735   2.507   0.0125 *  
Age                -0.005936   0.004146  -1.432   0.1528    
Gendermale          0.195165   0.079815   2.445   0.0148 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8323 on 538 degrees of freedom
Multiple R-squared:  0.02649,   Adjusted R-squared:  0.02106 
F-statistic:  4.88 on 3 and 538 DF,  p-value: 0.002345

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0.046963 
Standard error............: 0.018735 
Odds ratio (effect size)..: 1.048 
Lower 95% CI..............: 1.01 
Upper 95% CI..............: 1.087 
T-value...................: 2.506756 
P-value...................: 0.01247829 
R^2.......................: 0.026489 
Adjusted r^2..............: 0.02106 
Sample size of AE DB......: 2388 
Sample size of model......: 542 
Missing data %............: 77.30318 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age          Gendermale  
           5.24911            -0.11266            -0.01154             0.18294  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.15990 -0.51382  0.02868  0.55074  2.09503 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)         5.24911    0.28416  18.472  < 2e-16 ***
currentDF[, TRAIT] -0.11266    0.02236  -5.039 6.39e-07 ***
Age                -0.01154    0.00411  -2.809  0.00516 ** 
Gendermale          0.18294    0.07777   2.352  0.01902 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8187 on 544 degrees of freedom
Multiple R-squared:  0.06307,   Adjusted R-squared:  0.0579 
F-statistic: 12.21 on 3 and 544 DF,  p-value: 9.694e-08

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.112658 
Standard error............: 0.022359 
Odds ratio (effect size)..: 0.893 
Lower 95% CI..............: 0.855 
Upper 95% CI..............: 0.933 
T-value...................: -5.038517 
P-value...................: 6.394823e-07 
R^2.......................: 0.063065 
Adjusted r^2..............: 0.057899 
Sample size of AE DB......: 2388 
Sample size of model......: 548 
Missing data %............: 77.05193 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age          Gendermale  
          5.054615           -0.076923           -0.006881            0.239377  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3180 -0.5236  0.0519  0.5880  2.0237 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         5.054615   0.307454  16.440  < 2e-16 ***
currentDF[, TRAIT] -0.076923   0.052418  -1.467  0.14283    
Age                -0.006881   0.004145  -1.660  0.09744 .  
Gendermale          0.239377   0.079943   2.994  0.00288 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8369 on 536 degrees of freedom
Multiple R-squared:  0.02449,   Adjusted R-squared:  0.01903 
F-statistic: 4.485 on 3 and 536 DF,  p-value: 0.004028

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.076923 
Standard error............: 0.052418 
Odds ratio (effect size)..: 0.926 
Lower 95% CI..............: 0.836 
Upper 95% CI..............: 1.026 
T-value...................: -1.467485 
P-value...................: 0.1428307 
R^2.......................: 0.024489 
Adjusted r^2..............: 0.019029 
Sample size of AE DB......: 2388 
Sample size of model......: 540 
Missing data %............: 77.38693 

Analysis of IL6_pg_ug_2015_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -3.0631              0.1245  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.8417 -0.9325 -0.0252  0.8651  5.1402 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -2.905299   0.333376  -8.715  < 2e-16 ***
currentDF[, TRAIT]  0.123333   0.024351   5.065 4.79e-07 ***
Age                -0.002355   0.004747  -0.496    0.620    
Gendermale          0.003428   0.094980   0.036    0.971    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.445 on 1103 degrees of freedom
Multiple R-squared:  0.02367,   Adjusted R-squared:  0.02101 
F-statistic: 8.913 on 3 and 1103 DF,  p-value: 7.742e-06

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0.123333 
Standard error............: 0.024351 
Odds ratio (effect size)..: 1.131 
Lower 95% CI..............: 1.079 
Upper 95% CI..............: 1.187 
T-value...................: 5.064799 
P-value...................: 4.78904e-07 
R^2.......................: 0.023669 
Adjusted r^2..............: 0.021013 
Sample size of AE DB......: 2388 
Sample size of model......: 1107 
Missing data %............: 53.64322 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age  
         -2.686652           -0.126982           -0.007997  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.6298 -0.9666 -0.0141  0.9192  5.5295 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -2.675010   0.338711  -7.898 6.78e-15 ***
currentDF[, TRAIT] -0.127595   0.029628  -4.307 1.80e-05 ***
Age                -0.008007   0.004792  -1.671    0.095 .  
Gendermale         -0.015907   0.095341  -0.167    0.868    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.455 on 1114 degrees of freedom
Multiple R-squared:  0.01734,   Adjusted R-squared:  0.0147 
F-statistic: 6.553 on 3 and 1114 DF,  p-value: 0.0002158

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.127595 
Standard error............: 0.029628 
Odds ratio (effect size)..: 0.88 
Lower 95% CI..............: 0.831 
Upper 95% CI..............: 0.933 
T-value...................: -4.306616 
P-value...................: 1.803788e-05 
R^2.......................: 0.017342 
Adjusted r^2..............: 0.014696 
Sample size of AE DB......: 2388 
Sample size of model......: 1118 
Missing data %............: 53.18258 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
     -3.243  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.8345 -0.9328 -0.0131  0.9318  5.8822 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -2.877457   0.368338  -7.812 1.42e-14 ***
currentDF[, TRAIT] -0.064379   0.053023  -1.214    0.225    
Age                -0.003647   0.005075  -0.719    0.473    
Gendermale          0.007720   0.100992   0.076    0.939    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.473 on 999 degrees of freedom
Multiple R-squared:  0.00202,   Adjusted R-squared:  -0.0009774 
F-statistic: 0.6739 on 3 and 999 DF,  p-value: 0.5681

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.064379 
Standard error............: 0.053023 
Odds ratio (effect size)..: 0.938 
Lower 95% CI..............: 0.845 
Upper 95% CI..............: 1.04 
T-value...................: -1.214169 
P-value...................: 0.22497 
R^2.......................: 0.00202 
Adjusted r^2..............: -0.000977 
Sample size of AE DB......: 2388 
Sample size of model......: 1003 
Missing data %............: 57.99833 

Analysis of IL6R_pg_ug_2015_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
           -1.9115              0.1083  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.5941 -0.5052  0.1368  0.6836  3.4909 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -1.626395   0.264230  -6.155 1.05e-09 ***
currentDF[, TRAIT]  0.108785   0.018985   5.730 1.29e-08 ***
Age                -0.003074   0.003757  -0.818    0.413    
Gendermale         -0.103988   0.075059  -1.385    0.166    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.137 on 1101 degrees of freedom
Multiple R-squared:  0.03136,   Adjusted R-squared:  0.02872 
F-statistic: 11.88 on 3 and 1101 DF,  p-value: 1.164e-07

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0.108785 
Standard error............: 0.018985 
Odds ratio (effect size)..: 1.115 
Lower 95% CI..............: 1.074 
Upper 95% CI..............: 1.157 
T-value...................: 5.73014 
P-value...................: 1.294368e-08 
R^2.......................: 0.031358 
Adjusted r^2..............: 0.028718 
Sample size of AE DB......: 2388 
Sample size of model......: 1105 
Missing data %............: 53.72697 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
          -2.05286             0.05653  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.7438 -0.5087  0.1450  0.6970  3.5044 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -1.704077   0.268477  -6.347 3.19e-10 ***
currentDF[, TRAIT]  0.050503   0.023530   2.146   0.0321 *  
Age                -0.004681   0.003790  -1.235   0.2170    
Gendermale         -0.039226   0.075239  -0.521   0.6022    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.145 on 1113 degrees of freedom
Multiple R-squared:  0.006947,  Adjusted R-squared:  0.00427 
F-statistic: 2.595 on 3 and 1113 DF,  p-value: 0.05119

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: 0.050503 
Standard error............: 0.02353 
Odds ratio (effect size)..: 1.052 
Lower 95% CI..............: 1.004 
Upper 95% CI..............: 1.101 
T-value...................: 2.146317 
P-value...................: 0.03206336 
R^2.......................: 0.006947 
Adjusted r^2..............: 0.00427 
Sample size of AE DB......: 2388 
Sample size of model......: 1117 
Missing data %............: 53.22446 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age  
         -1.878208            0.134082           -0.005983  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.7594 -0.4963  0.1434  0.6755  3.4833 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -1.833764   0.294155  -6.234 6.71e-10 ***
currentDF[, TRAIT]  0.134992   0.042081   3.208  0.00138 ** 
Age                -0.005944   0.004021  -1.478  0.13964    
Gendermale         -0.070075   0.079798  -0.878  0.38007    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.159 on 995 degrees of freedom
Multiple R-squared:  0.01306,   Adjusted R-squared:  0.01008 
F-statistic: 4.389 on 3 and 995 DF,  p-value: 0.004452

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: 0.134992 
Standard error............: 0.042081 
Odds ratio (effect size)..: 1.145 
Lower 95% CI..............: 1.054 
Upper 95% CI..............: 1.243 
T-value...................: 3.207896 
P-value...................: 0.001379794 
R^2.......................: 0.01306 
Adjusted r^2..............: 0.010085 
Sample size of AE DB......: 2388 
Sample size of model......: 999 
Missing data %............: 58.16583 

Analysis of MCP1_pg_ug_2015_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)

Coefficients:
(Intercept)   Gendermale  
    -1.3260       0.1275  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.8350 -0.7858  0.1422  0.8745  3.5979 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -1.4014251  0.2973607  -4.713 2.74e-06 ***
currentDF[, TRAIT] -0.0174719  0.0215143  -0.812    0.417    
Age                 0.0007108  0.0042430   0.168    0.867    
Gendermale          0.1317384  0.0843699   1.561    0.119    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.314 on 1146 degrees of freedom
Multiple R-squared:  0.002625,  Adjusted R-squared:  1.403e-05 
F-statistic: 1.005 on 3 and 1146 DF,  p-value: 0.3896

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: -0.017472 
Standard error............: 0.021514 
Odds ratio (effect size)..: 0.983 
Lower 95% CI..............: 0.942 
Upper 95% CI..............: 1.025 
T-value...................: -0.812106 
P-value...................: 0.4168995 
R^2.......................: 0.002625 
Adjusted r^2..............: 1.4e-05 
Sample size of AE DB......: 2388 
Sample size of model......: 1150 
Missing data %............: 51.84255 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
          -1.24251            -0.08761  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.8716 -0.7610  0.1239  0.8561  3.6445 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -1.213542   0.298399  -4.067 5.09e-05 ***
currentDF[, TRAIT] -0.085281   0.026111  -3.266  0.00112 ** 
Age                -0.001408   0.004225  -0.333  0.73903    
Gendermale          0.098015   0.083613   1.172  0.24134    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.306 on 1158 degrees of freedom
Multiple R-squared:  0.01129,   Adjusted R-squared:  0.008724 
F-statistic: 4.406 on 3 and 1158 DF,  p-value: 0.004325

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.085281 
Standard error............: 0.026111 
Odds ratio (effect size)..: 0.918 
Lower 95% CI..............: 0.872 
Upper 95% CI..............: 0.966 
T-value...................: -3.266114 
P-value...................: 0.001122294 
R^2.......................: 0.011286 
Adjusted r^2..............: 0.008724 
Sample size of AE DB......: 2388 
Sample size of model......: 1162 
Missing data %............: 51.34003 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale  
           -1.1213             -0.1529              0.1742  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.6832 -0.7657  0.1143  0.8364  3.6994 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -1.1691408  0.3260608  -3.586 0.000352 ***
currentDF[, TRAIT] -0.1528943  0.0467150  -3.273 0.001100 ** 
Age                 0.0007009  0.0044823   0.156 0.875768    
Gendermale          0.1739403  0.0887300   1.960 0.050224 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.325 on 1038 degrees of freedom
Multiple R-squared:  0.0135,    Adjusted R-squared:  0.01065 
F-statistic: 4.735 on 3 and 1038 DF,  p-value: 0.002751

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.152894 
Standard error............: 0.046715 
Odds ratio (effect size)..: 0.858 
Lower 95% CI..............: 0.783 
Upper 95% CI..............: 0.941 
T-value...................: -3.272916 
P-value...................: 0.001099547 
R^2.......................: 0.013501 
Adjusted r^2..............: 0.01065 
Sample size of AE DB......: 2388 
Sample size of model......: 1042 
Missing data %............: 56.36516 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)  
     0.3781  

Degrees of Freedom: 454 Total (i.e. Null);  454 Residual
Null Deviance:      614.8 
Residual Deviance: 614.8    AIC: 616.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6505  -1.3011   0.9509   1.0358   1.1749  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)
(Intercept)          -0.285658   0.854132  -0.334    0.738
currentDF[, PROTEIN]  0.116941   0.092238   1.268    0.205
Age                   0.005461   0.011042   0.495    0.621
Gendermale           -0.232339   0.217428  -1.069    0.285

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 614.79  on 454  degrees of freedom
Residual deviance: 611.48  on 451  degrees of freedom
AIC: 619.48

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.116941 
Standard error............: 0.092238 
Odds ratio (effect size)..: 1.124 
Lower 95% CI..............: 0.938 
Upper 95% CI..............: 1.347 
Z-value...................: 1.267826 
P-value...................: 0.20486 
Hosmer and Lemeshow r^2...: 0.005383 
Cox and Snell r^2.........: 0.007248 
Nagelkerke's pseudo r^2...: 0.00978 
Sample size of AE DB......: 2388 
Sample size of model......: 455 
Missing data %............: 80.9464 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)  
      1.329  

Degrees of Freedom: 453 Total (i.e. Null);  453 Residual
Null Deviance:      465.8 
Residual Deviance: 465.8    AIC: 467.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9619   0.6186   0.6705   0.7060   0.8022  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)  
(Intercept)           2.15828    1.05000   2.056   0.0398 *
currentDF[, PROTEIN]  0.04290    0.11173   0.384   0.7010  
Age                  -0.01394    0.01353  -1.030   0.3030  
Gendermale           -0.07774    0.26262  -0.296   0.7672  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 465.77  on 453  degrees of freedom
Residual deviance: 464.42  on 450  degrees of freedom
AIC: 472.42

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.042896 
Standard error............: 0.111726 
Odds ratio (effect size)..: 1.044 
Lower 95% CI..............: 0.839 
Upper 95% CI..............: 1.299 
Z-value...................: 0.383945 
P-value...................: 0.7010193 
Hosmer and Lemeshow r^2...: 0.002889 
Cox and Snell r^2.........: 0.00296 
Nagelkerke's pseudo r^2...: 0.004613 
Sample size of AE DB......: 2388 
Sample size of model......: 454 
Missing data %............: 80.98828 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)   Gendermale  
     1.0468       0.5997  

Degrees of Freedom: 454 Total (i.e. Null);  453 Residual
Null Deviance:      441.2 
Residual Deviance: 435.7    AIC: 439.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9435   0.5812   0.5948   0.6079   0.8147  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)  
(Intercept)          0.782404   1.058021   0.739   0.4596  
currentDF[, PROTEIN] 0.033995   0.113930   0.298   0.7654  
Age                  0.001833   0.013807   0.133   0.8944  
Gendermale           0.607719   0.253718   2.395   0.0166 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 441.15  on 454  degrees of freedom
Residual deviance: 435.55  on 451  degrees of freedom
AIC: 443.55

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.033995 
Standard error............: 0.11393 
Odds ratio (effect size)..: 1.035 
Lower 95% CI..............: 0.828 
Upper 95% CI..............: 1.293 
Z-value...................: 0.298383 
P-value...................: 0.7654109 
Hosmer and Lemeshow r^2...: 0.012706 
Cox and Snell r^2.........: 0.012244 
Nagelkerke's pseudo r^2...: 0.019724 
Sample size of AE DB......: 2388 
Sample size of model......: 455 
Missing data %............: 80.9464 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)   Gendermale  
     0.6696       0.7091  

Degrees of Freedom: 454 Total (i.e. Null);  453 Residual
Null Deviance:      501 
Residual Deviance: 492  AIC: 496

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9621   0.5789   0.6611   0.7294   1.0949  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)   
(Intercept)          -0.19265    0.96939  -0.199  0.84247   
currentDF[, PROTEIN] -0.07423    0.10339  -0.718  0.47278   
Age                   0.01758    0.01269   1.385  0.16591   
Gendermale            0.68577    0.23472   2.922  0.00348 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 501.02  on 454  degrees of freedom
Residual deviance: 489.51  on 451  degrees of freedom
AIC: 497.51

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: IPH 
Effect size...............: -0.074227 
Standard error............: 0.103387 
Odds ratio (effect size)..: 0.928 
Lower 95% CI..............: 0.758 
Upper 95% CI..............: 1.137 
Z-value...................: -0.717958 
P-value...................: 0.4727831 
Hosmer and Lemeshow r^2...: 0.02297 
Cox and Snell r^2.........: 0.024977 
Nagelkerke's pseudo r^2...: 0.037417 
Sample size of AE DB......: 2388 
Sample size of model......: 455 
Missing data %............: 80.9464 

Analysis of MCP1_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN], 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]  
              1.2754               -0.1911  

Degrees of Freedom: 555 Total (i.e. Null);  554 Residual
Null Deviance:      749.7 
Residual Deviance: 746.3    AIC: 750.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.5761  -1.3102   0.9272   1.0229   1.3026  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)  
(Intercept)           0.38774    0.86096   0.450   0.6525  
currentDF[, PROTEIN] -0.17455    0.10503  -1.662   0.0965 .
Age                   0.01345    0.01008   1.334   0.1822  
Gendermale           -0.13028    0.19579  -0.665   0.5058  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 749.67  on 555  degrees of freedom
Residual deviance: 744.09  on 552  degrees of freedom
AIC: 752.09

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.174546 
Standard error............: 0.105033 
Odds ratio (effect size)..: 0.84 
Lower 95% CI..............: 0.684 
Upper 95% CI..............: 1.032 
Z-value...................: -1.661819 
P-value...................: 0.09654906 
Hosmer and Lemeshow r^2...: 0.007442 
Cox and Snell r^2.........: 0.009984 
Nagelkerke's pseudo r^2...: 0.013486 
Sample size of AE DB......: 2388 
Sample size of model......: 556 
Missing data %............: 76.71692 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN], 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]  
              4.7550               -0.6953  

Degrees of Freedom: 553 Total (i.e. Null);  552 Residual
Null Deviance:      538 
Residual Deviance: 512.7    AIC: 516.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5263   0.3932   0.5643   0.6954   1.1490  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)    
(Intercept)           5.87185    1.21117   4.848 1.25e-06 ***
currentDF[, PROTEIN] -0.70272    0.14786  -4.753 2.01e-06 ***
Age                  -0.01454    0.01314  -1.107    0.268    
Gendermale           -0.13269    0.25837  -0.514    0.608    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 537.98  on 553  degrees of freedom
Residual deviance: 511.18  on 550  degrees of freedom
AIC: 519.18

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.702722 
Standard error............: 0.147863 
Odds ratio (effect size)..: 0.495 
Lower 95% CI..............: 0.371 
Upper 95% CI..............: 0.662 
Z-value...................: -4.752516 
P-value...................: 2.009007e-06 
Hosmer and Lemeshow r^2...: 0.049807 
Cox and Snell r^2.........: 0.047216 
Nagelkerke's pseudo r^2...: 0.075992 
Sample size of AE DB......: 2388 
Sample size of model......: 554 
Missing data %............: 76.80067 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale  
             -2.3300                0.7694                0.5566  

Degrees of Freedom: 555 Total (i.e. Null);  553 Residual
Null Deviance:      538.8 
Residual Deviance: 496.6    AIC: 502.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4336   0.3796   0.5147   0.6558   1.5060  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -2.698592   1.086903  -2.483   0.0130 *  
currentDF[, PROTEIN]  0.772398   0.137592   5.614 1.98e-08 ***
Age                   0.005304   0.012912   0.411   0.6813    
Gendermale            0.553311   0.235382   2.351   0.0187 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 538.82  on 555  degrees of freedom
Residual deviance: 496.48  on 552  degrees of freedom
AIC: 504.48

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.772398 
Standard error............: 0.137592 
Odds ratio (effect size)..: 2.165 
Lower 95% CI..............: 1.653 
Upper 95% CI..............: 2.835 
Z-value...................: 5.613695 
P-value...................: 1.980514e-08 
Hosmer and Lemeshow r^2...: 0.078584 
Cox and Snell r^2.........: 0.073328 
Nagelkerke's pseudo r^2...: 0.118161 
Sample size of AE DB......: 2388 
Sample size of model......: 556 
Missing data %............: 76.71692 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)          Age   Gendermale  
   -0.76646      0.02073      0.78990  

Degrees of Freedom: 555 Total (i.e. Null);  553 Residual
Null Deviance:      611.8 
Residual Deviance: 594.4    AIC: 600.4

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0063   0.5568   0.6453   0.7253   1.1988  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -1.24578    0.98400  -1.266 0.205500    
currentDF[, PROTEIN]  0.09778    0.12043   0.812 0.416817    
Age                   0.02140    0.01159   1.847 0.064728 .  
Gendermale            0.77021    0.21170   3.638 0.000275 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 611.78  on 555  degrees of freedom
Residual deviance: 593.75  on 552  degrees of freedom
AIC: 601.75

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: IPH 
Effect size...............: 0.097784 
Standard error............: 0.12043 
Odds ratio (effect size)..: 1.103 
Lower 95% CI..............: 0.871 
Upper 95% CI..............: 1.396 
Z-value...................: 0.811956 
P-value...................: 0.4168168 
Hosmer and Lemeshow r^2...: 0.029473 
Cox and Snell r^2.........: 0.03191 
Nagelkerke's pseudo r^2...: 0.047824 
Sample size of AE DB......: 2388 
Sample size of model......: 556 
Missing data %............: 76.71692 

Analysis of IL6_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
            -1.07375              -0.07903               0.01345              -0.20223  

Degrees of Freedom: 1133 Total (i.e. Null);  1130 Residual
Null Deviance:      1572 
Residual Deviance: 1561     AIC: 1569

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.406  -1.152  -1.003   1.183   1.518  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)  
(Intercept)          -1.073750   0.475057  -2.260   0.0238 *
currentDF[, PROTEIN] -0.079029   0.041077  -1.924   0.0544 .
Age                   0.013455   0.006507   2.068   0.0387 *
Gendermale           -0.202231   0.129655  -1.560   0.1188  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1571.7  on 1133  degrees of freedom
Residual deviance: 1561.1  on 1130  degrees of freedom
AIC: 1569.1

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.079029 
Standard error............: 0.041077 
Odds ratio (effect size)..: 0.924 
Lower 95% CI..............: 0.853 
Upper 95% CI..............: 1.001 
Z-value...................: -1.923912 
P-value...................: 0.05436566 
Hosmer and Lemeshow r^2...: 0.006766 
Cox and Snell r^2.........: 0.009334 
Nagelkerke's pseudo r^2...: 0.012446 
Sample size of AE DB......: 2388 
Sample size of model......: 1134 
Missing data %............: 52.51256 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN], 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]  
              0.7118               -0.1947  

Degrees of Freedom: 1135 Total (i.e. Null);  1134 Residual
Null Deviance:      1172 
Residual Deviance: 1156     AIC: 1160

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1318   0.5468   0.6494   0.7162   1.0600  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)           0.593805   0.574870   1.033  0.30163    
currentDF[, PROTEIN] -0.193853   0.050658  -3.827  0.00013 ***
Age                   0.002485   0.007934   0.313  0.75416    
Gendermale           -0.071069   0.160474  -0.443  0.65786    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1171.5  on 1135  degrees of freedom
Residual deviance: 1156.2  on 1132  degrees of freedom
AIC: 1164.2

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.193853 
Standard error............: 0.050658 
Odds ratio (effect size)..: 0.824 
Lower 95% CI..............: 0.746 
Upper 95% CI..............: 0.91 
Z-value...................: -3.82672 
P-value...................: 0.0001298622 
Hosmer and Lemeshow r^2...: 0.013109 
Cox and Snell r^2.........: 0.013428 
Nagelkerke's pseudo r^2...: 0.020869 
Sample size of AE DB......: 2388 
Sample size of model......: 1136 
Missing data %............: 52.42881 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
             0.63818               0.34441               0.01408               0.86986  

Degrees of Freedom: 1135 Total (i.e. Null);  1132 Residual
Null Deviance:      1320 
Residual Deviance: 1231     AIC: 1239

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2463  -1.0758   0.6255   0.7981   1.4856  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)    
(Intercept)          0.638182   0.547585   1.165    0.244    
currentDF[, PROTEIN] 0.344406   0.050528   6.816 9.35e-12 ***
Age                  0.014081   0.007517   1.873    0.061 .  
Gendermale           0.869856   0.144292   6.028 1.66e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1319.7  on 1135  degrees of freedom
Residual deviance: 1231.1  on 1132  degrees of freedom
AIC: 1239.1

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.344406 
Standard error............: 0.050528 
Odds ratio (effect size)..: 1.411 
Lower 95% CI..............: 1.278 
Upper 95% CI..............: 1.558 
Z-value...................: 6.8162 
P-value...................: 9.34802e-12 
Hosmer and Lemeshow r^2...: 0.067178 
Cox and Snell r^2.........: 0.075074 
Nagelkerke's pseudo r^2...: 0.10927 
Sample size of AE DB......: 2388 
Sample size of model......: 1136 
Missing data %............: 52.42881 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)   Gendermale  
    0.02326      0.64534  

Degrees of Freedom: 1134 Total (i.e. Null);  1133 Residual
Null Deviance:      1514 
Residual Deviance: 1490     AIC: 1494

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.5574  -1.2367   0.8905   0.9307   1.2570  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.156562   0.486654  -0.322    0.748    
currentDF[, PROTEIN]  0.046192   0.042332   1.091    0.275    
Age                   0.004824   0.006697   0.720    0.471    
Gendermale            0.643408   0.131534   4.892    1e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1513.8  on 1134  degrees of freedom
Residual deviance: 1488.0  on 1131  degrees of freedom
AIC: 1496

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: 0.046192 
Standard error............: 0.042332 
Odds ratio (effect size)..: 1.047 
Lower 95% CI..............: 0.964 
Upper 95% CI..............: 1.138 
Z-value...................: 1.091195 
P-value...................: 0.2751873 
Hosmer and Lemeshow r^2...: 0.017028 
Cox and Snell r^2.........: 0.022456 
Nagelkerke's pseudo r^2...: 0.030489 
Sample size of AE DB......: 2388 
Sample size of model......: 1135 
Missing data %............: 52.47069 

Analysis of IL6R_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)          Age   Gendermale  
    -0.6992       0.0119      -0.2152  

Degrees of Freedom: 1133 Total (i.e. Null);  1131 Residual
Null Deviance:      1572 
Residual Deviance: 1566     AIC: 1572

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.346  -1.157  -1.038   1.190   1.363  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)  
(Intercept)          -0.655175   0.467724  -1.401   0.1613  
currentDF[, PROTEIN]  0.027653   0.051832   0.534   0.5937  
Age                   0.012070   0.006531   1.848   0.0646 .
Gendermale           -0.213914   0.129860  -1.647   0.0995 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1571.8  on 1133  degrees of freedom
Residual deviance: 1565.4  on 1130  degrees of freedom
AIC: 1573.4

Number of Fisher Scoring iterations: 3

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.027653 
Standard error............: 0.051832 
Odds ratio (effect size)..: 1.028 
Lower 95% CI..............: 0.929 
Upper 95% CI..............: 1.138 
Z-value...................: 0.533519 
P-value...................: 0.5936745 
Hosmer and Lemeshow r^2...: 0.00403 
Cox and Snell r^2.........: 0.00557 
Nagelkerke's pseudo r^2...: 0.007427 
Sample size of AE DB......: 2388 
Sample size of model......: 1134 
Missing data %............: 52.51256 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)  
      1.307  

Degrees of Freedom: 1135 Total (i.e. Null);  1135 Residual
Null Deviance:      1177 
Residual Deviance: 1177     AIC: 1179

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7994   0.6719   0.6873   0.6968   0.7493  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)  
(Intercept)          1.125140   0.565385   1.990   0.0466 *
currentDF[, PROTEIN] 0.025097   0.062561   0.401   0.6883  
Age                  0.003103   0.007909   0.392   0.6948  
Gendermale           0.029258   0.157523   0.186   0.8527  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1176.8  on 1135  degrees of freedom
Residual deviance: 1176.4  on 1132  degrees of freedom
AIC: 1184.4

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.025097 
Standard error............: 0.062561 
Odds ratio (effect size)..: 1.025 
Lower 95% CI..............: 0.907 
Upper 95% CI..............: 1.159 
Z-value...................: 0.40116 
P-value...................: 0.6883026 
Hosmer and Lemeshow r^2...: 0.000281 
Cox and Snell r^2.........: 0.000291 
Nagelkerke's pseudo r^2...: 0.000451 
Sample size of AE DB......: 2388 
Sample size of model......: 1136 
Missing data %............: 52.42881 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
            -0.20010               0.12004               0.01319               0.82396  

Degrees of Freedom: 1135 Total (i.e. Null);  1132 Residual
Null Deviance:      1324 
Residual Deviance: 1284     AIC: 1292

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8885  -1.2943   0.6856   0.7625   1.2713  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.200102   0.523490  -0.382   0.7023    
currentDF[, PROTEIN]  0.120041   0.057677   2.081   0.0374 *  
Age                   0.013186   0.007375   1.788   0.0738 .  
Gendermale            0.823957   0.141082   5.840 5.21e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1323.7  on 1135  degrees of freedom
Residual deviance: 1283.6  on 1132  degrees of freedom
AIC: 1291.6

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.120041 
Standard error............: 0.057677 
Odds ratio (effect size)..: 1.128 
Lower 95% CI..............: 1.007 
Upper 95% CI..............: 1.262 
Z-value...................: 2.081254 
P-value...................: 0.03741064 
Hosmer and Lemeshow r^2...: 0.030306 
Cox and Snell r^2.........: 0.034698 
Nagelkerke's pseudo r^2...: 0.050422 
Sample size of AE DB......: 2388 
Sample size of model......: 1136 
Missing data %............: 52.42881 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale  
              0.2894                0.1198                0.6029  

Degrees of Freedom: 1133 Total (i.e. Null);  1131 Residual
Null Deviance:      1515 
Residual Deviance: 1489     AIC: 1495

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6030  -1.2677   0.8841   0.9491   1.4022  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)    
(Intercept)          0.105057   0.481377   0.218   0.8272    
currentDF[, PROTEIN] 0.120979   0.053273   2.271   0.0232 *  
Age                  0.002723   0.006743   0.404   0.6863    
Gendermale           0.602572   0.132109   4.561 5.09e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1514.7  on 1133  degrees of freedom
Residual deviance: 1489.1  on 1130  degrees of freedom
AIC: 1497.1

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: 0.120979 
Standard error............: 0.053273 
Odds ratio (effect size)..: 1.129 
Lower 95% CI..............: 1.017 
Upper 95% CI..............: 1.253 
Z-value...................: 2.27091 
P-value...................: 0.02315245 
Hosmer and Lemeshow r^2...: 0.016865 
Cox and Snell r^2.........: 0.022275 
Nagelkerke's pseudo r^2...: 0.030223 
Sample size of AE DB......: 2388 
Sample size of model......: 1134 
Missing data %............: 52.51256 

Analysis of MCP1_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
            -1.30828              -0.34278               0.01426              -0.19161  

Degrees of Freedom: 1178 Total (i.e. Null);  1175 Residual
Null Deviance:      1634 
Residual Deviance: 1572     AIC: 1580

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9932  -1.1065  -0.7866   1.1439   1.7832  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -1.308282   0.466334  -2.805  0.00502 ** 
currentDF[, PROTEIN] -0.342779   0.048334  -7.092 1.32e-12 ***
Age                   0.014260   0.006535   2.182  0.02910 *  
Gendermale           -0.191606   0.129659  -1.478  0.13947    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1633.9  on 1178  degrees of freedom
Residual deviance: 1572.1  on 1175  degrees of freedom
AIC: 1580.1

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.342779 
Standard error............: 0.048334 
Odds ratio (effect size)..: 0.71 
Lower 95% CI..............: 0.646 
Upper 95% CI..............: 0.78 
Z-value...................: -7.09191 
P-value...................: 1.322732e-12 
Hosmer and Lemeshow r^2...: 0.037814 
Cox and Snell r^2.........: 0.051054 
Nagelkerke's pseudo r^2...: 0.068083 
Sample size of AE DB......: 2388 
Sample size of model......: 1179 
Missing data %............: 50.62814 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN], 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]  
               1.106                -0.188  

Degrees of Freedom: 1180 Total (i.e. Null);  1179 Residual
Null Deviance:      1217 
Residual Deviance: 1205     AIC: 1209

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2374   0.5500   0.6626   0.7178   0.9142  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)           0.971527   0.550319   1.765  0.07750 . 
currentDF[, PROTEIN] -0.187980   0.057268  -3.282  0.00103 **
Age                   0.001926   0.007783   0.247  0.80459   
Gendermale            0.004089   0.155715   0.026  0.97905   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1216.6  on 1180  degrees of freedom
Residual deviance: 1205.3  on 1177  degrees of freedom
AIC: 1213.3

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.18798 
Standard error............: 0.057268 
Odds ratio (effect size)..: 0.829 
Lower 95% CI..............: 0.741 
Upper 95% CI..............: 0.927 
Z-value...................: -3.282465 
P-value...................: 0.001029038 
Hosmer and Lemeshow r^2...: 0.00926 
Cox and Snell r^2.........: 0.009494 
Nagelkerke's pseudo r^2...: 0.014764 
Sample size of AE DB......: 2388 
Sample size of model......: 1181 
Missing data %............: 50.54439 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
            -0.17789               0.12296               0.01114               0.84851  

Degrees of Freedom: 1180 Total (i.e. Null);  1177 Residual
Null Deviance:      1384 
Residual Deviance: 1336     AIC: 1344

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9359  -1.2862   0.6765   0.7691   1.2274  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.177889   0.507537  -0.350   0.7260    
currentDF[, PROTEIN]  0.122961   0.050168   2.451   0.0142 *  
Age                   0.011138   0.007195   1.548   0.1216    
Gendermale            0.848506   0.137295   6.180  6.4e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1383.8  on 1180  degrees of freedom
Residual deviance: 1336.3  on 1177  degrees of freedom
AIC: 1344.3

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.122961 
Standard error............: 0.050168 
Odds ratio (effect size)..: 1.131 
Lower 95% CI..............: 1.025 
Upper 95% CI..............: 1.248 
Z-value...................: 2.450975 
P-value...................: 0.01424698 
Hosmer and Lemeshow r^2...: 0.034374 
Cox and Snell r^2.........: 0.039478 
Nagelkerke's pseudo r^2...: 0.0572 
Sample size of AE DB......: 2388 
Sample size of model......: 1181 
Missing data %............: 50.54439 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale  
            -0.07727              -0.08375               0.62569  

Degrees of Freedom: 1178 Total (i.e. Null);  1176 Residual
Null Deviance:      1576 
Residual Deviance: 1550     AIC: 1556

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6739  -1.2642   0.8917   0.9478   1.2963  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.274165   0.465648  -0.589   0.5560    
currentDF[, PROTEIN] -0.083655   0.046529  -1.798   0.0722 .  
Age                   0.002880   0.006573   0.438   0.6613    
Gendermale            0.624913   0.128837   4.850 1.23e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1576.2  on 1178  degrees of freedom
Residual deviance: 1549.9  on 1175  degrees of freedom
AIC: 1557.9

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: -0.083655 
Standard error............: 0.046529 
Odds ratio (effect size)..: 0.92 
Lower 95% CI..............: 0.84 
Upper 95% CI..............: 1.008 
Z-value...................: -1.797891 
P-value...................: 0.07219422 
Hosmer and Lemeshow r^2...: 0.016644 
Cox and Snell r^2.........: 0.022005 
Nagelkerke's pseudo r^2...: 0.029844 
Sample size of AE DB......: 2388 
Sample size of model......: 1179 
Missing data %............: 50.62814 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)

Coefficients:
(Intercept)   Gendermale  
     0.1147      -0.1457  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.6651 -0.7037 -0.0163  0.6872  3.0540 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)         0.265124   0.327047   0.811    0.418
currentDF[, TRAIT] -0.043457   0.038912  -1.117    0.265
Age                -0.002260   0.004714  -0.479    0.632
Gendermale         -0.139309   0.092751  -1.502    0.134

Residual standard error: 0.9589 on 523 degrees of freedom
Multiple R-squared:  0.007532,  Adjusted R-squared:  0.001839 
F-statistic: 1.323 on 3 and 523 DF,  p-value: 0.266

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: -0.043457 
Standard error............: 0.038912 
Odds ratio (effect size)..: 0.957 
Lower 95% CI..............: 0.887 
Upper 95% CI..............: 1.033 
T-value...................: -1.116809 
P-value...................: 0.2645889 
R^2.......................: 0.007532 
Adjusted r^2..............: 0.001839 
Sample size of AE DB......: 2388 
Sample size of model......: 527 
Missing data %............: 77.93132 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)

Coefficients:
(Intercept)   Gendermale  
     0.1254      -0.1467  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.66930 -0.69331 -0.00069  0.66680  2.97072 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)         0.247782   0.339493   0.730    0.466
currentDF[, TRAIT]  0.023438   0.040868   0.573    0.567
Age                -0.001916   0.004866  -0.394    0.694
Gendermale         -0.140271   0.094251  -1.488    0.137

Residual standard error: 0.964 on 519 degrees of freedom
Multiple R-squared:  0.005833,  Adjusted R-squared:  8.659e-05 
F-statistic: 1.015 on 3 and 519 DF,  p-value: 0.3856

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: 0.023438 
Standard error............: 0.040868 
Odds ratio (effect size)..: 1.024 
Lower 95% CI..............: 0.945 
Upper 95% CI..............: 1.109 
T-value...................: 0.573496 
P-value...................: 0.5665574 
R^2.......................: 0.005833 
Adjusted r^2..............: 8.7e-05 
Sample size of AE DB......: 2388 
Sample size of model......: 523 
Missing data %............: 78.09883 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)

Coefficients:
(Intercept)   Gendermale  
     0.1137      -0.1357  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.65204 -0.71205 -0.00561  0.69258  3.04629 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)         0.335120   0.335382   0.999    0.318
currentDF[, TRAIT] -0.033064   0.052781  -0.626    0.531
Age                -0.003204   0.004831  -0.663    0.508
Gendermale         -0.135313   0.094253  -1.436    0.152

Residual standard error: 0.966 on 511 degrees of freedom
Multiple R-squared:  0.005607,  Adjusted R-squared:  -0.0002313 
F-statistic: 0.9604 on 3 and 511 DF,  p-value: 0.4112

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.033064 
Standard error............: 0.052781 
Odds ratio (effect size)..: 0.967 
Lower 95% CI..............: 0.872 
Upper 95% CI..............: 1.073 
T-value...................: -0.626443 
P-value...................: 0.5313039 
R^2.......................: 0.005607 
Adjusted r^2..............: -0.000231 
Sample size of AE DB......: 2388 
Sample size of model......: 515 
Missing data %............: 78.43384 

Analysis of MCP1_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age          Gendermale  
           0.34006             0.10727            -0.00823             0.28612  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.66459 -0.66985 -0.00544  0.65388  2.97049 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)   
(Intercept)         0.340061   0.327464   1.038  0.29950   
currentDF[, TRAIT]  0.107266   0.038479   2.788  0.00549 **
Age                -0.008230   0.004734  -1.738  0.08271 . 
Gendermale          0.286120   0.091290   3.134  0.00181 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.98 on 560 degrees of freedom
Multiple R-squared:  0.03666,   Adjusted R-squared:  0.0315 
F-statistic: 7.105 on 3 and 560 DF,  p-value: 0.0001085

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.107266 
Standard error............: 0.038479 
Odds ratio (effect size)..: 1.113 
Lower 95% CI..............: 1.032 
Upper 95% CI..............: 1.2 
T-value...................: 2.787615 
P-value...................: 0.005490063 
R^2.......................: 0.036664 
Adjusted r^2..............: 0.031504 
Sample size of AE DB......: 2388 
Sample size of model......: 564 
Missing data %............: 76.38191 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age          Gendermale  
           0.74540            -0.18145            -0.01324             0.23454  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.67204 -0.64103 -0.03139  0.64140  2.69183 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.74540    0.33159   2.248  0.02497 *  
currentDF[, TRAIT] -0.18145    0.03959  -4.583 5.65e-06 ***
Age                -0.01324    0.00477  -2.775  0.00570 ** 
Gendermale          0.23454    0.09053   2.591  0.00982 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9621 on 556 degrees of freedom
Multiple R-squared:  0.05804,   Adjusted R-squared:  0.05296 
F-statistic: 11.42 on 3 and 556 DF,  p-value: 2.82e-07

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.18145 
Standard error............: 0.039588 
Odds ratio (effect size)..: 0.834 
Lower 95% CI..............: 0.772 
Upper 95% CI..............: 0.901 
T-value...................: -4.583453 
P-value...................: 5.652515e-06 
R^2.......................: 0.058039 
Adjusted r^2..............: 0.052957 
Sample size of AE DB......: 2388 
Sample size of model......: 560 
Missing data %............: 76.54941 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age          Gendermale  
           0.39679            -0.09755            -0.00880             0.31637  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.69246 -0.65643  0.00031  0.65368  2.74036 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.39679    0.33186   1.196  0.23235    
currentDF[, TRAIT] -0.09755    0.05111  -1.909  0.05680 .  
Age                -0.00880    0.00479  -1.837  0.06674 .  
Gendermale          0.31637    0.09248   3.421  0.00067 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9842 on 549 degrees of freedom
Multiple R-squared:  0.03246,   Adjusted R-squared:  0.02717 
F-statistic: 6.139 on 3 and 549 DF,  p-value: 0.0004131

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.097554 
Standard error............: 0.051106 
Odds ratio (effect size)..: 0.907 
Lower 95% CI..............: 0.821 
Upper 95% CI..............: 1.003 
T-value...................: -1.908837 
P-value...................: 0.05680423 
R^2.......................: 0.032459 
Adjusted r^2..............: 0.027172 
Sample size of AE DB......: 2388 
Sample size of model......: 553 
Missing data %............: 76.84255 

Analysis of IL6_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
         0.0008327           0.1298916  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2750 -0.6882  0.0017  0.6410  3.6437 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.080646   0.226702   0.356    0.722    
currentDF[, TRAIT]  0.128610   0.029926   4.298 1.88e-05 ***
Age                -0.001259   0.003217  -0.391    0.696    
Gendermale          0.009559   0.064377   0.148    0.882    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9916 on 1124 degrees of freedom
Multiple R-squared:  0.01684,   Adjusted R-squared:  0.01421 
F-statistic: 6.417 on 3 and 1124 DF,  p-value: 0.0002611

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.12861 
Standard error............: 0.029926 
Odds ratio (effect size)..: 1.137 
Lower 95% CI..............: 1.072 
Upper 95% CI..............: 1.206 
T-value...................: 4.297634 
P-value...................: 1.875797e-05 
R^2.......................: 0.016839 
Adjusted r^2..............: 0.014215 
Sample size of AE DB......: 2388 
Sample size of model......: 1128 
Missing data %............: 52.76382 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age  
          0.379605           -0.140378           -0.005529  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0677 -0.6892 -0.0049  0.6794  3.1705 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.386998   0.231838   1.669   0.0953 .  
currentDF[, TRAIT] -0.141045   0.031339  -4.501 7.48e-06 ***
Age                -0.005538   0.003272  -1.692   0.0909 .  
Gendermale         -0.009751   0.064944  -0.150   0.8807    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9908 on 1120 degrees of freedom
Multiple R-squared:  0.01862,   Adjusted R-squared:  0.01599 
F-statistic: 7.084 on 3 and 1120 DF,  p-value: 0.0001021

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.141045 
Standard error............: 0.031339 
Odds ratio (effect size)..: 0.868 
Lower 95% CI..............: 0.817 
Upper 95% CI..............: 0.923 
T-value...................: -4.5006 
P-value...................: 7.483581e-06 
R^2.......................: 0.018623 
Adjusted r^2..............: 0.015994 
Sample size of AE DB......: 2388 
Sample size of model......: 1124 
Missing data %............: 52.93132 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
         -0.005939           -0.045061  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2631 -0.6756  0.0016  0.6855  3.3859 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)
(Intercept)         0.113002   0.237668   0.475    0.635
currentDF[, TRAIT] -0.045329   0.031251  -1.450    0.147
Age                -0.001943   0.003369  -0.577    0.564
Gendermale          0.020800   0.067259   0.309    0.757

Residual standard error: 1.002 on 1048 degrees of freedom
Multiple R-squared:  0.002385,  Adjusted R-squared:  -0.0004703 
F-statistic: 0.8353 on 3 and 1048 DF,  p-value: 0.4745

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.045329 
Standard error............: 0.031251 
Odds ratio (effect size)..: 0.956 
Lower 95% CI..............: 0.899 
Upper 95% CI..............: 1.016 
T-value...................: -1.450483 
P-value...................: 0.147223 
R^2.......................: 0.002385 
Adjusted r^2..............: -0.00047 
Sample size of AE DB......: 2388 
Sample size of model......: 1052 
Missing data %............: 55.9464 

Analysis of IL6R_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
          0.006105            0.171717  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1452 -0.6436 -0.0070  0.6466  3.3803 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.323725   0.226682   1.428    0.154    
currentDF[, TRAIT]  0.171595   0.029416   5.833 7.09e-09 ***
Age                -0.003819   0.003210  -1.190    0.234    
Gendermale         -0.078695   0.064098  -1.228    0.220    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9838 on 1124 degrees of freedom
Multiple R-squared:  0.03229,   Adjusted R-squared:  0.02971 
F-statistic:  12.5 on 3 and 1124 DF,  p-value: 4.811e-08

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.171595 
Standard error............: 0.029416 
Odds ratio (effect size)..: 1.187 
Lower 95% CI..............: 1.121 
Upper 95% CI..............: 1.258 
T-value...................: 5.833439 
P-value...................: 7.092794e-09 
R^2.......................: 0.03229 
Adjusted r^2..............: 0.029707 
Sample size of AE DB......: 2388 
Sample size of model......: 1128 
Missing data %............: 52.76382 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
          0.003564            0.085455  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2788 -0.6560  0.0049  0.6817  3.3960 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)  
(Intercept)         0.279731   0.234166   1.195   0.2325  
currentDF[, TRAIT]  0.077483   0.031462   2.463   0.0139 *
Age                -0.003775   0.003299  -1.145   0.2526  
Gendermale         -0.023816   0.065312  -0.365   0.7154  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9939 on 1120 degrees of freedom
Multiple R-squared:  0.008145,  Adjusted R-squared:  0.005488 
F-statistic: 3.066 on 3 and 1120 DF,  p-value: 0.0272

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: 0.077483 
Standard error............: 0.031462 
Odds ratio (effect size)..: 1.081 
Lower 95% CI..............: 1.016 
Upper 95% CI..............: 1.149 
T-value...................: 2.462754 
P-value...................: 0.0139371 
R^2.......................: 0.008145 
Adjusted r^2..............: 0.005488 
Sample size of AE DB......: 2388 
Sample size of model......: 1124 
Missing data %............: 52.93132 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age  
          0.363777            0.108548           -0.005145  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3980 -0.6565 -0.0131  0.6619  3.3784 

Coefficients:
                   Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.40529    0.23903   1.696  0.09027 .  
currentDF[, TRAIT]  0.10889    0.03133   3.476  0.00053 ***
Age                -0.00514    0.00338  -1.521  0.12864    
Gendermale         -0.05983    0.06724  -0.890  0.37373    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9985 on 1047 degrees of freedom
Multiple R-squared:  0.01437,   Adjusted R-squared:  0.01154 
F-statistic: 5.087 on 3 and 1047 DF,  p-value: 0.001687

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: 0.108891 
Standard error............: 0.03133 
Odds ratio (effect size)..: 1.115 
Lower 95% CI..............: 1.049 
Upper 95% CI..............: 1.186 
T-value...................: 3.475655 
P-value...................: 0.0005304978 
R^2.......................: 0.014366 
Adjusted r^2..............: 0.011542 
Sample size of AE DB......: 2388 
Sample size of model......: 1051 
Missing data %............: 55.98828 

Analysis of MCP1_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale  
          -0.08851            -0.04407             0.10833  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4091 -0.6867  0.0027  0.6638  3.3363 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)  
(Intercept)        -0.0521469  0.2245097  -0.232   0.8164  
currentDF[, TRAIT] -0.0445154  0.0293223  -1.518   0.1292  
Age                -0.0005313  0.0031888  -0.167   0.8677  
Gendermale          0.1084819  0.0633932   1.711   0.0873 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9994 on 1168 degrees of freedom
Multiple R-squared:  0.004169,  Adjusted R-squared:  0.001611 
F-statistic:  1.63 on 3 and 1168 DF,  p-value: 0.1807

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: -0.044515 
Standard error............: 0.029322 
Odds ratio (effect size)..: 0.956 
Lower 95% CI..............: 0.903 
Upper 95% CI..............: 1.013 
T-value...................: -1.518139 
P-value...................: 0.1292499 
R^2.......................: 0.004169 
Adjusted r^2..............: 0.001611 
Sample size of AE DB......: 2388 
Sample size of model......: 1172 
Missing data %............: 50.92127 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]  
          -0.01583            -0.11198  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4371 -0.6552 -0.0098  0.6354  3.3670 

Coefficients:
                    Estimate Std. Error t value Pr(>|t|)    
(Intercept)         0.103027   0.228379   0.451 0.651986    
currentDF[, TRAIT] -0.111746   0.030611  -3.651 0.000273 ***
Age                -0.002390   0.003226  -0.741 0.458971    
Gendermale          0.065262   0.063722   1.024 0.305970    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9951 on 1164 degrees of freedom
Multiple R-squared:  0.01332,   Adjusted R-squared:  0.01078 
F-statistic: 5.238 on 3 and 1164 DF,  p-value: 0.001359

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.111746 
Standard error............: 0.030611 
Odds ratio (effect size)..: 0.894 
Lower 95% CI..............: 0.842 
Upper 95% CI..............: 0.95 
T-value...................: -3.650566 
P-value...................: 0.0002732734 
R^2.......................: 0.013321 
Adjusted r^2..............: 0.010778 
Sample size of AE DB......: 2388 
Sample size of model......: 1168 
Missing data %............: 51.08878 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale  
           -0.1167             -0.1300              0.1283  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2795 -0.6726  0.0001  0.6322  3.4098 

Coefficients:
                     Estimate Std. Error t value Pr(>|t|)    
(Intercept)        -0.1460528  0.2332909  -0.626   0.5314    
currentDF[, TRAIT] -0.1299743  0.0305087  -4.260 2.22e-05 ***
Age                 0.0004283  0.0033099   0.129   0.8971    
Gendermale          0.1282621  0.0656979   1.952   0.0512 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.002 on 1090 degrees of freedom
Multiple R-squared:  0.01952,   Adjusted R-squared:  0.01682 
F-statistic: 7.234 on 3 and 1090 DF,  p-value: 8.293e-05

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.129974 
Standard error............: 0.030509 
Odds ratio (effect size)..: 0.878 
Lower 95% CI..............: 0.827 
Upper 95% CI..............: 0.932 
T-value...................: -4.260235 
P-value...................: 2.218457e-05 
R^2.......................: 0.019522 
Adjusted r^2..............: 0.016823 
Sample size of AE DB......: 2388 
Sample size of model......: 1094 
Missing data %............: 54.1876 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN], 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]  
              0.3364                0.1700  

Degrees of Freedom: 527 Total (i.e. Null);  526 Residual
Null Deviance:      717.2 
Residual Deviance: 713.8    AIC: 717.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6055  -1.2824   0.9442   1.0453   1.2408  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)  
(Intercept)          -0.39815    0.69417  -0.574   0.5663  
currentDF[, PROTEIN]  0.16861    0.09329   1.807   0.0707 .
Age                   0.01255    0.01003   1.251   0.2108  
Gendermale           -0.15185    0.19853  -0.765   0.4443  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.23  on 527  degrees of freedom
Residual deviance: 711.68  on 524  degrees of freedom
AIC: 719.68

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.168614 
Standard error............: 0.093291 
Odds ratio (effect size)..: 1.184 
Lower 95% CI..............: 0.986 
Upper 95% CI..............: 1.421 
Z-value...................: 1.807395 
P-value...................: 0.07070067 
Hosmer and Lemeshow r^2...: 0.007736 
Cox and Snell r^2.........: 0.010453 
Nagelkerke's pseudo r^2...: 0.01407 
Sample size of AE DB......: 2388 
Sample size of model......: 528 
Missing data %............: 77.88945 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)  
      1.439  

Degrees of Freedom: 526 Total (i.e. Null);  526 Residual
Null Deviance:      515 
Residual Deviance: 515  AIC: 517

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0039   0.5622   0.6281   0.6781   0.8143  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)   
(Intercept)           2.50639    0.90034   2.784  0.00537 **
currentDF[, PROTEIN] -0.15233    0.11589  -1.314  0.18871   
Age                  -0.01370    0.01281  -1.069  0.28490   
Gendermale           -0.18207    0.25283  -0.720  0.47146   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 514.99  on 526  degrees of freedom
Residual deviance: 511.76  on 523  degrees of freedom
AIC: 519.76

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.152332 
Standard error............: 0.115895 
Odds ratio (effect size)..: 0.859 
Lower 95% CI..............: 0.684 
Upper 95% CI..............: 1.078 
Z-value...................: -1.314401 
P-value...................: 0.1887114 
Hosmer and Lemeshow r^2...: 0.006272 
Cox and Snell r^2.........: 0.00611 
Nagelkerke's pseudo r^2...: 0.009798 
Sample size of AE DB......: 2388 
Sample size of model......: 527 
Missing data %............: 77.93132 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)   Gendermale  
     0.8473       0.8007  

Degrees of Freedom: 527 Total (i.e. Null);  526 Residual
Null Deviance:      529.5 
Residual Deviance: 517.4    AIC: 521.4

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0323   0.5426   0.5923   0.6460   0.9468  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)    
(Intercept)          0.633935   0.848167   0.747 0.454812    
currentDF[, PROTEIN] 0.142074   0.114411   1.242 0.214314    
Age                  0.002994   0.012321   0.243 0.807985    
Gendermale           0.822167   0.227638   3.612 0.000304 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 529.53  on 527  degrees of freedom
Residual deviance: 515.77  on 524  degrees of freedom
AIC: 523.77

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.142074 
Standard error............: 0.114411 
Odds ratio (effect size)..: 1.153 
Lower 95% CI..............: 0.921 
Upper 95% CI..............: 1.442 
Z-value...................: 1.24179 
P-value...................: 0.214314 
Hosmer and Lemeshow r^2...: 0.025995 
Cox and Snell r^2.........: 0.025733 
Nagelkerke's pseudo r^2...: 0.040641 
Sample size of AE DB......: 2388 
Sample size of model......: 528 
Missing data %............: 77.88945 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)          Age   Gendermale  
   -1.53026      0.03187      0.77077  

Degrees of Freedom: 527 Total (i.e. Null);  525 Residual
Null Deviance:      589.4 
Residual Deviance: 569.3    AIC: 575.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0272   0.5126   0.6441   0.7617   1.3318  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -1.531376   0.788651  -1.942 0.052165 .  
currentDF[, PROTEIN]  0.004591   0.106067   0.043 0.965479    
Age                   0.031879   0.011544   2.761 0.005754 ** 
Gendermale            0.771453   0.215860   3.574 0.000352 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 589.39  on 527  degrees of freedom
Residual deviance: 569.29  on 524  degrees of freedom
AIC: 577.29

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.004591 
Standard error............: 0.106067 
Odds ratio (effect size)..: 1.005 
Lower 95% CI..............: 0.816 
Upper 95% CI..............: 1.237 
Z-value...................: 0.043279 
P-value...................: 0.9654788 
Hosmer and Lemeshow r^2...: 0.034097 
Cox and Snell r^2.........: 0.037347 
Nagelkerke's pseudo r^2...: 0.055534 
Sample size of AE DB......: 2388 
Sample size of model......: 528 
Missing data %............: 77.88945 

Analysis of MCP1_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)          Age  
   -0.66894      0.01552  

Degrees of Freedom: 564 Total (i.e. Null);  563 Residual
Null Deviance:      763.6 
Residual Deviance: 761.1    AIC: 765.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.5354  -1.3063   0.9435   1.0328   1.2735  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)
(Intercept)          -0.520510   0.681656  -0.764    0.445
currentDF[, PROTEIN] -0.084572   0.087486  -0.967    0.334
Age                   0.015036   0.009908   1.518    0.129
Gendermale           -0.159870   0.193286  -0.827    0.408

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 763.63  on 564  degrees of freedom
Residual deviance: 759.27  on 561  degrees of freedom
AIC: 767.27

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.084572 
Standard error............: 0.087486 
Odds ratio (effect size)..: 0.919 
Lower 95% CI..............: 0.774 
Upper 95% CI..............: 1.091 
Z-value...................: -0.966696 
P-value...................: 0.3336962 
Hosmer and Lemeshow r^2...: 0.005706 
Cox and Snell r^2.........: 0.007683 
Nagelkerke's pseudo r^2...: 0.010366 
Sample size of AE DB......: 2388 
Sample size of model......: 565 
Missing data %............: 76.34003 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN], 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]  
              1.5378               -0.5379  

Degrees of Freedom: 562 Total (i.e. Null);  561 Residual
Null Deviance:      547.6 
Residual Deviance: 524.3    AIC: 528.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4625   0.4102   0.5643   0.6848   1.1638  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)    
(Intercept)           2.84690    0.91211   3.121   0.0018 ** 
currentDF[, PROTEIN] -0.54841    0.11723  -4.678  2.9e-06 ***
Age                  -0.01803    0.01302  -1.384   0.1663    
Gendermale           -0.11699    0.25442  -0.460   0.6456    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 547.57  on 562  degrees of freedom
Residual deviance: 522.07  on 559  degrees of freedom
AIC: 530.07

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.548413 
Standard error............: 0.117233 
Odds ratio (effect size)..: 0.578 
Lower 95% CI..............: 0.459 
Upper 95% CI..............: 0.727 
Z-value...................: -4.677968 
P-value...................: 2.897316e-06 
Hosmer and Lemeshow r^2...: 0.046582 
Cox and Snell r^2.........: 0.044294 
Nagelkerke's pseudo r^2...: 0.071224 
Sample size of AE DB......: 2388 
Sample size of model......: 563 
Missing data %............: 76.42379 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale  
              1.1894                0.6807                0.5650  

Degrees of Freedom: 564 Total (i.e. Null);  562 Residual
Null Deviance:      554.2 
Residual Deviance: 509.3    AIC: 515.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4929   0.3605   0.5225   0.6691   1.3725  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)    
(Intercept)           0.85428    0.86465   0.988   0.3231    
currentDF[, PROTEIN]  0.68303    0.12124   5.634 1.76e-08 ***
Age                   0.00499    0.01257   0.397   0.6914    
Gendermale            0.56378    0.23128   2.438   0.0148 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 554.19  on 564  degrees of freedom
Residual deviance: 509.11  on 561  degrees of freedom
AIC: 517.11

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.68303 
Standard error............: 0.12124 
Odds ratio (effect size)..: 1.98 
Lower 95% CI..............: 1.561 
Upper 95% CI..............: 2.511 
Z-value...................: 5.633721 
P-value...................: 1.763624e-08 
Hosmer and Lemeshow r^2...: 0.081344 
Cox and Snell r^2.........: 0.076687 
Nagelkerke's pseudo r^2...: 0.122697 
Sample size of AE DB......: 2388 
Sample size of model......: 565 
Missing data %............: 76.34003 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)          Age   Gendermale  
   -1.02619      0.02446      0.78389  

Degrees of Freedom: 564 Total (i.e. Null);  562 Residual
Null Deviance:      625.9 
Residual Deviance: 607.1    AIC: 613.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0414   0.5339   0.6471   0.7406   1.2555  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -1.06199    0.77564  -1.369 0.170946    
currentDF[, PROTEIN]  0.10109    0.10144   0.997 0.318965    
Age                   0.02532    0.01139   2.224 0.026177 *  
Gendermale            0.75630    0.20942   3.611 0.000304 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 625.93  on 564  degrees of freedom
Residual deviance: 606.14  on 561  degrees of freedom
AIC: 614.14

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.101091 
Standard error............: 0.101437 
Odds ratio (effect size)..: 1.106 
Lower 95% CI..............: 0.907 
Upper 95% CI..............: 1.35 
Z-value...................: 0.996588 
P-value...................: 0.3189645 
Hosmer and Lemeshow r^2...: 0.031618 
Cox and Snell r^2.........: 0.034421 
Nagelkerke's pseudo r^2...: 0.051395 
Sample size of AE DB......: 2388 
Sample size of model......: 565 
Missing data %............: 76.34003 

Analysis of IL6_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
            -0.82326              -0.11365               0.01351              -0.20124  

Degrees of Freedom: 1133 Total (i.e. Null);  1130 Residual
Null Deviance:      1572 
Residual Deviance: 1561     AIC: 1569

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.405  -1.151  -1.013   1.182   1.494  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)  
(Intercept)          -0.823262   0.458227  -1.797   0.0724 .
currentDF[, PROTEIN] -0.113647   0.059989  -1.894   0.0582 .
Age                   0.013512   0.006506   2.077   0.0378 *
Gendermale           -0.201235   0.129656  -1.552   0.1206  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1571.7  on 1133  degrees of freedom
Residual deviance: 1561.2  on 1130  degrees of freedom
AIC: 1569.2

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.113647 
Standard error............: 0.059989 
Odds ratio (effect size)..: 0.893 
Lower 95% CI..............: 0.794 
Upper 95% CI..............: 1.004 
Z-value...................: -1.89445 
P-value...................: 0.05816533 
Hosmer and Lemeshow r^2...: 0.006689 
Cox and Snell r^2.........: 0.009228 
Nagelkerke's pseudo r^2...: 0.012306 
Sample size of AE DB......: 2388 
Sample size of model......: 1134 
Missing data %............: 52.51256 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN], 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]  
              1.3407               -0.2859  

Degrees of Freedom: 1135 Total (i.e. Null);  1134 Residual
Null Deviance:      1172 
Residual Deviance: 1156     AIC: 1160

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0925   0.5434   0.6477   0.7187   0.9893  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)           1.209758   0.559055   2.164 0.030469 *  
currentDF[, PROTEIN] -0.284656   0.074277  -3.832 0.000127 ***
Age                   0.002611   0.007932   0.329 0.742003    
Gendermale           -0.068954   0.160475  -0.430 0.667424    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1171.5  on 1135  degrees of freedom
Residual deviance: 1156.1  on 1132  degrees of freedom
AIC: 1164.1

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.284656 
Standard error............: 0.074277 
Odds ratio (effect size)..: 0.752 
Lower 95% CI..............: 0.65 
Upper 95% CI..............: 0.87 
Z-value...................: -3.832368 
P-value...................: 0.0001269155 
Hosmer and Lemeshow r^2...: 0.013161 
Cox and Snell r^2.........: 0.013481 
Nagelkerke's pseudo r^2...: 0.020951 
Sample size of AE DB......: 2388 
Sample size of model......: 1136 
Missing data %............: 52.42881 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
            -0.45642               0.49681               0.01386               0.86551  

Degrees of Freedom: 1135 Total (i.e. Null);  1132 Residual
Null Deviance:      1320 
Residual Deviance: 1232     AIC: 1240

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2318  -1.0860   0.6253   0.8001   1.4676  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.456424   0.525551  -0.868   0.3851    
currentDF[, PROTEIN]  0.496810   0.072834   6.821 9.03e-12 ***
Age                   0.013863   0.007517   1.844   0.0651 .  
Gendermale            0.865510   0.144282   5.999 1.99e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1319.7  on 1135  degrees of freedom
Residual deviance: 1231.7  on 1132  degrees of freedom
AIC: 1239.7

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.49681 
Standard error............: 0.072834 
Odds ratio (effect size)..: 1.643 
Lower 95% CI..............: 1.425 
Upper 95% CI..............: 1.896 
Z-value...................: 6.821123 
P-value...................: 9.033127e-12 
Hosmer and Lemeshow r^2...: 0.066674 
Cox and Snell r^2.........: 0.074533 
Nagelkerke's pseudo r^2...: 0.108482 
Sample size of AE DB......: 2388 
Sample size of model......: 1136 
Missing data %............: 52.42881 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)   Gendermale  
    0.02326      0.64534  

Degrees of Freedom: 1134 Total (i.e. Null);  1133 Residual
Null Deviance:      1514 
Residual Deviance: 1490     AIC: 1494

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.5453  -1.2340   0.8904   0.9307   1.2566  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.302686   0.470800  -0.643    0.520    
currentDF[, PROTEIN]  0.065082   0.061907   1.051    0.293    
Age                   0.004786   0.006696   0.715    0.475    
Gendermale            0.642790   0.131532   4.887 1.02e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1513.8  on 1134  degrees of freedom
Residual deviance: 1488.1  on 1131  degrees of freedom
AIC: 1496.1

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.065082 
Standard error............: 0.061907 
Odds ratio (effect size)..: 1.067 
Lower 95% CI..............: 0.945 
Upper 95% CI..............: 1.205 
Z-value...................: 1.051285 
P-value...................: 0.2931276 
Hosmer and Lemeshow r^2...: 0.016972 
Cox and Snell r^2.........: 0.022382 
Nagelkerke's pseudo r^2...: 0.030389 
Sample size of AE DB......: 2388 
Sample size of model......: 1135 
Missing data %............: 52.47069 

Analysis of IL6R_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)          Age   Gendermale  
   -0.70308      0.01195     -0.21761  

Degrees of Freedom: 1134 Total (i.e. Null);  1132 Residual
Null Deviance:      1573 
Residual Deviance: 1567     AIC: 1573

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.351  -1.156  -1.036   1.190   1.372  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)  
(Intercept)          -0.722969   0.461171  -1.568   0.1170  
currentDF[, PROTEIN]  0.050413   0.059798   0.843   0.3992  
Age                   0.012220   0.006533   1.871   0.0614 .
Gendermale           -0.215830   0.129855  -1.662   0.0965 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1573.1  on 1134  degrees of freedom
Residual deviance: 1566.3  on 1131  degrees of freedom
AIC: 1574.3

Number of Fisher Scoring iterations: 3

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.050413 
Standard error............: 0.059798 
Odds ratio (effect size)..: 1.052 
Lower 95% CI..............: 0.935 
Upper 95% CI..............: 1.182 
Z-value...................: 0.843058 
P-value...................: 0.3991959 
Hosmer and Lemeshow r^2...: 0.004357 
Cox and Snell r^2.........: 0.006021 
Nagelkerke's pseudo r^2...: 0.008028 
Sample size of AE DB......: 2388 
Sample size of model......: 1135 
Missing data %............: 52.47069 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)  
      1.303  

Degrees of Freedom: 1136 Total (i.e. Null);  1136 Residual
Null Deviance:      1180 
Residual Deviance: 1180     AIC: 1182

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8412   0.6608   0.6871   0.7021   0.7501  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)  
(Intercept)          1.055209   0.556645   1.896    0.058 .
currentDF[, PROTEIN] 0.054395   0.072780   0.747    0.455  
Age                  0.003366   0.007898   0.426    0.670  
Gendermale           0.024221   0.157433   0.154    0.878  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1179.9  on 1136  degrees of freedom
Residual deviance: 1179.1  on 1133  degrees of freedom
AIC: 1187.1

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.054395 
Standard error............: 0.07278 
Odds ratio (effect size)..: 1.056 
Lower 95% CI..............: 0.916 
Upper 95% CI..............: 1.218 
Z-value...................: 0.74739 
P-value...................: 0.4548283 
Hosmer and Lemeshow r^2...: 0.000619 
Cox and Snell r^2.........: 0.000642 
Nagelkerke's pseudo r^2...: 0.000995 
Sample size of AE DB......: 2388 
Sample size of model......: 1137 
Missing data %............: 52.38694 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
            -0.44254               0.13586               0.01312               0.82461  

Degrees of Freedom: 1136 Total (i.e. Null);  1133 Residual
Null Deviance:      1324 
Residual Deviance: 1284     AIC: 1292

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9008  -1.2841   0.6888   0.7635   1.2184  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.442537   0.517161  -0.856   0.3922    
currentDF[, PROTEIN]  0.135860   0.068675   1.978   0.0479 *  
Age                   0.013117   0.007375   1.779   0.0753 .  
Gendermale            0.824605   0.141034   5.847 5.01e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1324.4  on 1136  degrees of freedom
Residual deviance: 1284.5  on 1133  degrees of freedom
AIC: 1292.5

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.13586 
Standard error............: 0.068675 
Odds ratio (effect size)..: 1.146 
Lower 95% CI..............: 1.001 
Upper 95% CI..............: 1.311 
Z-value...................: 1.97829 
P-value...................: 0.04789604 
Hosmer and Lemeshow r^2...: 0.030128 
Cox and Snell r^2.........: 0.034484 
Nagelkerke's pseudo r^2...: 0.050122 
Sample size of AE DB......: 2388 
Sample size of model......: 1137 
Missing data %............: 52.38694 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale  
             0.04247               0.15197               0.60545  

Degrees of Freedom: 1134 Total (i.e. Null);  1132 Residual
Null Deviance:      1516 
Residual Deviance: 1489     AIC: 1495

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6477  -1.2918   0.8750   0.9619   1.3214  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.145527   0.475505  -0.306   0.7596    
currentDF[, PROTEIN]  0.153359   0.062198   2.466   0.0137 *  
Age                   0.002740   0.006747   0.406   0.6847    
Gendermale            0.605104   0.132160   4.579 4.68e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1515.7  on 1134  degrees of freedom
Residual deviance: 1489.0  on 1131  degrees of freedom
AIC: 1497

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.153359 
Standard error............: 0.062198 
Odds ratio (effect size)..: 1.166 
Lower 95% CI..............: 1.032 
Upper 95% CI..............: 1.317 
Z-value...................: 2.46566 
P-value...................: 0.0136761 
Hosmer and Lemeshow r^2...: 0.017572 
Cox and Snell r^2.........: 0.023192 
Nagelkerke's pseudo r^2...: 0.031471 
Sample size of AE DB......: 2388 
Sample size of model......: 1135 
Missing data %............: 52.47069 

Analysis of MCP1_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
            -0.87403              -0.45247               0.01396              -0.19322  

Degrees of Freedom: 1178 Total (i.e. Null);  1175 Residual
Null Deviance:      1634 
Residual Deviance: 1571     AIC: 1579

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7850  -1.1164  -0.7578   1.1280   1.9083  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.874028   0.460395  -1.898   0.0576 .  
currentDF[, PROTEIN] -0.452467   0.062870  -7.197 6.16e-13 ***
Age                   0.013956   0.006536   2.135   0.0327 *  
Gendermale           -0.193224   0.129705  -1.490   0.1363    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1633.9  on 1178  degrees of freedom
Residual deviance: 1571.0  on 1175  degrees of freedom
AIC: 1579

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.452467 
Standard error............: 0.06287 
Odds ratio (effect size)..: 0.636 
Lower 95% CI..............: 0.562 
Upper 95% CI..............: 0.719 
Z-value...................: -7.196888 
P-value...................: 6.160221e-13 
Hosmer and Lemeshow r^2...: 0.03853 
Cox and Snell r^2.........: 0.051997 
Nagelkerke's pseudo r^2...: 0.069339 
Sample size of AE DB......: 2388 
Sample size of model......: 1179 
Missing data %............: 50.62814 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN], 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]  
              1.3322               -0.2276  

Degrees of Freedom: 1180 Total (i.e. Null);  1179 Residual
Null Deviance:      1217 
Residual Deviance: 1207     AIC: 1211

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0999   0.5754   0.6578   0.7138   0.9467  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)   
(Intercept)           1.210693   0.546856   2.214  0.02683 * 
currentDF[, PROTEIN] -0.227373   0.072305  -3.145  0.00166 **
Age                   0.001751   0.007781   0.225  0.82191   
Gendermale            0.001944   0.155632   0.012  0.99004   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1216.6  on 1180  degrees of freedom
Residual deviance: 1206.5  on 1177  degrees of freedom
AIC: 1214.5

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.227373 
Standard error............: 0.072305 
Odds ratio (effect size)..: 0.797 
Lower 95% CI..............: 0.691 
Upper 95% CI..............: 0.918 
Z-value...................: -3.144645 
P-value...................: 0.001662884 
Hosmer and Lemeshow r^2...: 0.008293 
Cox and Snell r^2.........: 0.008507 
Nagelkerke's pseudo r^2...: 0.013229 
Sample size of AE DB......: 2388 
Sample size of model......: 1181 
Missing data %............: 50.54439 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
            -0.33492               0.16339               0.01125               0.84917  

Degrees of Freedom: 1180 Total (i.e. Null);  1177 Residual
Null Deviance:      1384 
Residual Deviance: 1336     AIC: 1344

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9529  -1.2790   0.6795   0.7714   1.2276  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.334921   0.503185  -0.666   0.5057    
currentDF[, PROTEIN]  0.163390   0.066951   2.440   0.0147 *  
Age                   0.011250   0.007195   1.564   0.1179    
Gendermale            0.849174   0.137286   6.185 6.19e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1383.8  on 1180  degrees of freedom
Residual deviance: 1336.3  on 1177  degrees of freedom
AIC: 1344.3

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.16339 
Standard error............: 0.066951 
Odds ratio (effect size)..: 1.177 
Lower 95% CI..............: 1.033 
Upper 95% CI..............: 1.343 
Z-value...................: 2.440433 
P-value...................: 0.01466968 
Hosmer and Lemeshow r^2...: 0.034383 
Cox and Snell r^2.........: 0.039487 
Nagelkerke's pseudo r^2...: 0.057213 
Sample size of AE DB......: 2388 
Sample size of model......: 1181 
Missing data %............: 50.54439 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale  
             0.02354              -0.12418               0.62708  

Degrees of Freedom: 1178 Total (i.e. Null);  1176 Residual
Null Deviance:      1576 
Residual Deviance: 1549     AIC: 1555

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6449  -1.2648   0.8841   0.9534   1.3387  

Coefficients:
                      Estimate Std. Error z value Pr(>|z|)    
(Intercept)          -0.166656   0.462054  -0.361   0.7183    
currentDF[, PROTEIN] -0.123887   0.060696  -2.041   0.0412 *  
Age                   0.002781   0.006577   0.423   0.6725    
Gendermale            0.626312   0.128886   4.859 1.18e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1576.2  on 1178  degrees of freedom
Residual deviance: 1549.0  on 1175  degrees of freedom
AIC: 1557

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: -0.123887 
Standard error............: 0.060696 
Odds ratio (effect size)..: 0.883 
Lower 95% CI..............: 0.784 
Upper 95% CI..............: 0.995 
Z-value...................: -2.041093 
P-value...................: 0.04124156 
Hosmer and Lemeshow r^2...: 0.017232 
Cox and Snell r^2.........: 0.022773 
Nagelkerke's pseudo r^2...: 0.030886 
Sample size of AE DB......: 2388 
Sample size of model......: 1179 
Missing data %............: 50.62814 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, and stenosis.

Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_LN.

- processing Macrophages_LN
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(COVARIATES_M2)` instead of `COVARIATES_M2` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.

Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + Med.all.antiplatelet + 
    GFR_MDRD + CAD_history, data = currentDF)

Coefficients:
            (Intercept)               Gendermale  Med.all.antiplateletyes                 GFR_MDRD              CAD_history  
               4.103909                -0.284834                -0.271084                 0.004152                 0.163807  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9032 -0.7308 -0.0446  0.6661  4.5590 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                5.328129   1.065723   5.000 8.77e-07 ***
currentDF[, TRAIT]        -0.033490   0.028026  -1.195   0.2328    
Age                       -0.001666   0.007477  -0.223   0.8238    
Gendermale                -0.298080   0.127682  -2.335   0.0201 *  
Hypertension.compositeyes  0.020986   0.169028   0.124   0.9013    
DiabetesStatusDiabetes     0.067662   0.142911   0.473   0.6362    
SmokerCurrentyes           0.023604   0.122913   0.192   0.8478    
Med.Statin.LLDyes         -0.136625   0.127000  -1.076   0.2827    
Med.all.antiplateletyes   -0.319954   0.197708  -1.618   0.1064    
GFR_MDRD                   0.004767   0.003269   1.458   0.1456    
BMI                       -0.008503   0.015540  -0.547   0.5846    
CAD_history                0.210670   0.122440   1.721   0.0861 .  
Stroke_history             0.046045   0.116922   0.394   0.6939    
Peripheral.interv         -0.044833   0.137875  -0.325   0.7452    
stenose50-70%             -0.896091   0.705483  -1.270   0.2048    
stenose70-90%             -0.913710   0.641683  -1.424   0.1553    
stenose90-99%             -0.866203   0.639556  -1.354   0.1764    
stenose100% (Occlusion)   -1.283157   0.814781  -1.575   0.1161    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.079 on 382 degrees of freedom
Multiple R-squared:  0.03732,   Adjusted R-squared:  -0.005525 
F-statistic: 0.871 on 17 and 382 DF,  p-value: 0.6091

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: -0.03349 
Standard error............: 0.028026 
Odds ratio (effect size)..: 0.967 
Lower 95% CI..............: 0.915 
Upper 95% CI..............: 1.022 
T-value...................: -1.194968 
P-value...................: 0.2328408 
R^2.......................: 0.037317 
Adjusted r^2..............: -0.005525 
Sample size of AE DB......: 2388 
Sample size of model......: 400 
Missing data %............: 83.24958 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + GFR_MDRD + CAD_history, 
    data = currentDF)

Coefficients:
(Intercept)   Gendermale     GFR_MDRD  CAD_history  
   3.833812    -0.284067     0.004641     0.185825  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9791 -0.7354 -0.0493  0.6983  4.5102 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                5.631403   1.039466   5.418 1.06e-07 ***
currentDF[, TRAIT]         0.008979   0.034238   0.262   0.7933    
Age                       -0.003700   0.007322  -0.505   0.6136    
Gendermale                -0.307441   0.124996  -2.460   0.0143 *  
Hypertension.compositeyes -0.017251   0.161017  -0.107   0.9147    
DiabetesStatusDiabetes     0.041644   0.139749   0.298   0.7659    
SmokerCurrentyes          -0.022435   0.120142  -0.187   0.8520    
Med.Statin.LLDyes         -0.133992   0.123235  -1.087   0.2776    
Med.all.antiplateletyes   -0.289321   0.190195  -1.521   0.1290    
GFR_MDRD                   0.005066   0.003233   1.567   0.1179    
BMI                       -0.012998   0.014999  -0.867   0.3867    
CAD_history                0.210577   0.121129   1.738   0.0829 .  
Stroke_history             0.029454   0.114635   0.257   0.7974    
Peripheral.interv          0.005405   0.137903   0.039   0.9688    
stenose50-70%             -0.969032   0.692204  -1.400   0.1623    
stenose70-90%             -0.907907   0.634095  -1.432   0.1530    
stenose90-99%             -0.829748   0.631393  -1.314   0.1896    
stenose100% (Occlusion)   -1.259466   0.804880  -1.565   0.1184    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.066 on 388 degrees of freedom
Multiple R-squared:  0.03691,   Adjusted R-squared:  -0.005284 
F-statistic: 0.8748 on 17 and 388 DF,  p-value: 0.6046

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: 0.008979 
Standard error............: 0.034238 
Odds ratio (effect size)..: 1.009 
Lower 95% CI..............: 0.944 
Upper 95% CI..............: 1.079 
T-value...................: 0.262263 
P-value...................: 0.7932581 
R^2.......................: 0.036913 
Adjusted r^2..............: -0.005284 
Sample size of AE DB......: 2388 
Sample size of model......: 406 
Missing data %............: 82.99832 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + GFR_MDRD + CAD_history, 
    data = currentDF)

Coefficients:
(Intercept)   Gendermale     GFR_MDRD  CAD_history  
   3.756093    -0.247568     0.005238     0.172172  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9331 -0.7357 -0.0333  0.7108  4.5826 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                5.629836   1.065900   5.282 2.15e-07 ***
currentDF[, TRAIT]        -0.024028   0.084334  -0.285   0.7759    
Age                       -0.004541   0.007427  -0.611   0.5413    
Gendermale                -0.269618   0.126774  -2.127   0.0341 *  
Hypertension.compositeyes  0.001877   0.167583   0.011   0.9911    
DiabetesStatusDiabetes     0.067339   0.144271   0.467   0.6409    
SmokerCurrentyes          -0.007169   0.122842  -0.058   0.9535    
Med.Statin.LLDyes         -0.127639   0.125637  -1.016   0.3103    
Med.all.antiplateletyes   -0.254284   0.196881  -1.292   0.1973    
GFR_MDRD                   0.005475   0.003304   1.657   0.0984 .  
BMI                       -0.013560   0.015302  -0.886   0.3761    
CAD_history                0.203887   0.123226   1.655   0.0988 .  
Stroke_history             0.029287   0.116544   0.251   0.8017    
Peripheral.interv          0.001324   0.139245   0.010   0.9924    
stenose50-70%             -0.966805   0.702240  -1.377   0.1694    
stenose70-90%             -0.915169   0.643578  -1.422   0.1558    
stenose90-99%             -0.847577   0.641374  -1.322   0.1871    
stenose100% (Occlusion)   -0.915754   0.854866  -1.071   0.2847    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.082 on 382 degrees of freedom
Multiple R-squared:  0.03248,   Adjusted R-squared:  -0.01058 
F-statistic: 0.7542 on 17 and 382 DF,  p-value: 0.7457

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.024028 
Standard error............: 0.084334 
Odds ratio (effect size)..: 0.976 
Lower 95% CI..............: 0.828 
Upper 95% CI..............: 1.152 
T-value...................: -0.284921 
P-value...................: 0.7758592 
R^2.......................: 0.032475 
Adjusted r^2..............: -0.010582 
Sample size of AE DB......: 2388 
Sample size of model......: 400 
Missing data %............: 83.24958 

Analysis of MCP1_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + Med.Statin.LLD + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale  Hypertension.compositeyes  
                 5.103134                   0.043436                  -0.006408                   0.203966                  -0.195643  
        Med.Statin.LLDyes    Med.all.antiplateletyes  
                -0.223055                   0.201318  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2496 -0.5165  0.0401  0.5857  1.9749 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                5.910e+00  7.475e-01   7.906 1.86e-14 ***
currentDF[, TRAIT]         4.105e-02  2.006e-02   2.046  0.04131 *  
Age                       -9.148e-03  5.079e-03  -1.801  0.07234 .  
Gendermale                 1.826e-01  8.723e-02   2.094  0.03681 *  
Hypertension.compositeyes -2.086e-01  1.170e-01  -1.783  0.07527 .  
DiabetesStatusDiabetes    -3.236e-02  9.732e-02  -0.333  0.73965    
SmokerCurrentyes          -7.826e-02  8.394e-02  -0.932  0.35162    
Med.Statin.LLDyes         -2.377e-01  8.938e-02  -2.660  0.00808 ** 
Med.all.antiplateletyes    1.718e-01  1.384e-01   1.241  0.21507    
GFR_MDRD                  -1.361e-05  2.153e-03  -0.006  0.99496    
BMI                       -1.398e-02  1.043e-02  -1.340  0.18093    
CAD_history                1.112e-01  8.576e-02   1.297  0.19533    
Stroke_history             5.039e-02  8.112e-02   0.621  0.53476    
Peripheral.interv         -1.334e-01  1.007e-01  -1.325  0.18578    
stenose50-70%             -4.240e-01  5.363e-01  -0.791  0.42956    
stenose70-90%             -1.815e-01  4.951e-01  -0.367  0.71402    
stenose90-99%             -1.605e-01  4.939e-01  -0.325  0.74531    
stenose100% (Occlusion)   -7.515e-01  6.258e-01  -1.201  0.23035    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8387 on 476 degrees of freedom
Multiple R-squared:  0.06479,   Adjusted R-squared:  0.03139 
F-statistic:  1.94 on 17 and 476 DF,  p-value: 0.01348

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0.041046 
Standard error............: 0.020062 
Odds ratio (effect size)..: 1.042 
Lower 95% CI..............: 1.002 
Upper 95% CI..............: 1.084 
T-value...................: 2.04592 
P-value...................: 0.04131326 
R^2.......................: 0.064791 
Adjusted r^2..............: 0.03139 
Sample size of AE DB......: 2388 
Sample size of model......: 494 
Missing data %............: 79.31323 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + Med.Statin.LLD + Med.all.antiplatelet + 
    CAD_history, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale  Hypertension.compositeyes  
                  5.38138                   -0.11600                   -0.01216                    0.18033                   -0.20127  
        Med.Statin.LLDyes    Med.all.antiplateletyes                CAD_history  
                 -0.21292                    0.23166                    0.12469  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.07018 -0.53655  0.03467  0.56632  2.02007 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                6.127787   0.724585   8.457 3.28e-16 ***
currentDF[, TRAIT]        -0.114944   0.023405  -4.911 1.24e-06 ***
Age                       -0.014207   0.004927  -2.884  0.00411 ** 
Gendermale                 0.167671   0.084432   1.986  0.04761 *  
Hypertension.compositeyes -0.200013   0.111043  -1.801  0.07229 .  
DiabetesStatusDiabetes    -0.058669   0.094300  -0.622  0.53413    
SmokerCurrentyes          -0.090731   0.080994  -1.120  0.26318    
Med.Statin.LLDyes         -0.212011   0.085906  -2.468  0.01393 *  
Med.all.antiplateletyes    0.191079   0.131899   1.449  0.14807    
GFR_MDRD                   0.000200   0.002098   0.095  0.92410    
BMI                       -0.012451   0.009998  -1.245  0.21363    
CAD_history                0.147609   0.083959   1.758  0.07936 .  
Stroke_history             0.059535   0.078689   0.757  0.44967    
Peripheral.interv         -0.131218   0.098917  -1.327  0.18528    
stenose50-70%             -0.391836   0.520961  -0.752  0.45233    
stenose70-90%             -0.194782   0.483665  -0.403  0.68733    
stenose90-99%             -0.192270   0.482198  -0.399  0.69026    
stenose100% (Occlusion)   -0.888011   0.610951  -1.453  0.14674    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8194 on 483 degrees of freedom
Multiple R-squared:  0.1051,    Adjusted R-squared:  0.07359 
F-statistic: 3.336 on 17 and 483 DF,  p-value: 8.398e-06

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.114944 
Standard error............: 0.023405 
Odds ratio (effect size)..: 0.891 
Lower 95% CI..............: 0.851 
Upper 95% CI..............: 0.933 
T-value...................: -4.911116 
P-value...................: 1.241414e-06 
R^2.......................: 0.105086 
Adjusted r^2..............: 0.073588 
Sample size of AE DB......: 2388 
Sample size of model......: 501 
Missing data %............: 79.0201 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + Med.Statin.LLD + 
    Med.all.antiplatelet, data = currentDF)

Coefficients:
            (Intercept)                      Age               Gendermale        Med.Statin.LLDyes  Med.all.antiplateletyes  
               4.888582                -0.007793                 0.255881                -0.203922                 0.244368  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2320 -0.5447  0.0209  0.5708  2.0150 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                5.8306386  0.7488051   7.787 4.35e-14 ***
currentDF[, TRAIT]        -0.0736783  0.0560723  -1.314  0.18948    
Age                       -0.0098492  0.0050290  -1.958  0.05076 .  
Gendermale                 0.2312103  0.0868748   2.661  0.00804 ** 
Hypertension.compositeyes -0.1699790  0.1163269  -1.461  0.14462    
DiabetesStatusDiabetes    -0.0201171  0.0977882  -0.206  0.83710    
SmokerCurrentyes          -0.0917532  0.0839957  -1.092  0.27523    
Med.Statin.LLDyes         -0.2113847  0.0886662  -2.384  0.01752 *  
Med.all.antiplateletyes    0.2070417  0.1380296   1.500  0.13428    
GFR_MDRD                   0.0003182  0.0021989   0.145  0.88501    
BMI                       -0.0119131  0.0103569  -1.150  0.25061    
CAD_history                0.1289650  0.0869698   1.483  0.13877    
Stroke_history             0.0606234  0.0813170   0.746  0.45633    
Peripheral.interv         -0.1181870  0.1019860  -1.159  0.24710    
stenose50-70%             -0.4420833  0.5339946  -0.828  0.40815    
stenose70-90%             -0.1544408  0.4958562  -0.311  0.75559    
stenose90-99%             -0.1365629  0.4945868  -0.276  0.78258    
stenose100% (Occlusion)   -0.7433279  0.6260776  -1.187  0.23571    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8397 on 475 degrees of freedom
Multiple R-squared:  0.06341,   Adjusted R-squared:  0.02989 
F-statistic: 1.892 on 17 and 475 DF,  p-value: 0.01684

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.073678 
Standard error............: 0.056072 
Odds ratio (effect size)..: 0.929 
Lower 95% CI..............: 0.832 
Upper 95% CI..............: 1.037 
T-value...................: -1.313989 
P-value...................: 0.1894841 
R^2.......................: 0.06341 
Adjusted r^2..............: 0.02989 
Sample size of AE DB......: 2388 
Sample size of model......: 493 
Missing data %............: 79.35511 

Analysis of IL6_pg_ug_2015_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    SmokerCurrent + GFR_MDRD + CAD_history + Stroke_history + 
    Peripheral.interv, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes           SmokerCurrentyes                   GFR_MDRD  
                -2.690821                   0.102632                  -0.223751                   0.160849                  -0.003282  
              CAD_history             Stroke_history          Peripheral.interv  
                -0.191410                   0.240282                  -0.212187  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.7026 -0.9256  0.0110  0.8741  4.8263 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -2.0771142  0.9346824  -2.222 0.026499 *  
currentDF[, TRAIT]         0.0985178  0.0257145   3.831 0.000136 ***
Age                       -0.0060114  0.0057952  -1.037 0.299858    
Gendermale                 0.0071293  0.1033093   0.069 0.944997    
Hypertension.compositeyes -0.1970016  0.1441350  -1.367 0.172014    
DiabetesStatusDiabetes     0.0009241  0.1129358   0.008 0.993473    
SmokerCurrentyes           0.1158940  0.1040658   1.114 0.265705    
Med.Statin.LLDyes         -0.0907882  0.1142685  -0.795 0.427092    
Med.all.antiplateletyes   -0.0191074  0.1538261  -0.124 0.901172    
GFR_MDRD                  -0.0040316  0.0024571  -1.641 0.101172    
BMI                       -0.0135699  0.0129652  -1.047 0.295528    
CAD_history               -0.1594178  0.1058927  -1.505 0.132535    
Stroke_history             0.2574278  0.0996182   2.584 0.009909 ** 
Peripheral.interv         -0.2109443  0.1231333  -1.713 0.087011 .  
stenose50-70%             -0.0560786  0.6716629  -0.083 0.933478    
stenose70-90%              0.3243743  0.6488074   0.500 0.617222    
stenose90-99%              0.2435948  0.6485229   0.376 0.707287    
stenose100% (Occlusion)    0.9026959  0.8476817   1.065 0.287190    
stenose50-99%              0.0402124  1.2045494   0.033 0.973376    
stenose70-99%             -0.2334215  0.8737346  -0.267 0.789408    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.437 on 956 degrees of freedom
Multiple R-squared:  0.04869,   Adjusted R-squared:  0.02979 
F-statistic: 2.575 on 19 and 956 DF,  p-value: 0.0002445

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0.098518 
Standard error............: 0.025715 
Odds ratio (effect size)..: 1.104 
Lower 95% CI..............: 1.049 
Upper 95% CI..............: 1.161 
T-value...................: 3.831213 
P-value...................: 0.0001358352 
R^2.......................: 0.048694 
Adjusted r^2..............: 0.029787 
Sample size of AE DB......: 2388 
Sample size of model......: 976 
Missing data %............: 59.12898 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    CAD_history + Stroke_history + Peripheral.interv, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age         CAD_history      Stroke_history   Peripheral.interv  
          -2.49336            -0.14407            -0.01057            -0.20573             0.27847            -0.21314  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.4261 -0.9451  0.0344  0.9068  5.2333 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -2.078646   0.931604  -2.231  0.02589 *  
currentDF[, TRAIT]        -0.147154   0.031400  -4.686 3.18e-06 ***
Age                       -0.011799   0.005781  -2.041  0.04154 *  
Gendermale                -0.041451   0.103403  -0.401  0.68860    
Hypertension.compositeyes -0.120224   0.142206  -0.845  0.39808    
DiabetesStatusDiabetes    -0.006793   0.112792  -0.060  0.95199    
SmokerCurrentyes           0.098763   0.103683   0.953  0.34105    
Med.Statin.LLDyes         -0.085970   0.112710  -0.763  0.44580    
Med.all.antiplateletyes   -0.028053   0.153119  -0.183  0.85467    
GFR_MDRD                  -0.003391   0.002467  -1.375  0.16953    
BMI                       -0.011670   0.012829  -0.910  0.36324    
CAD_history               -0.158405   0.106023  -1.494  0.13549    
Stroke_history             0.278403   0.099099   2.809  0.00506 ** 
Peripheral.interv         -0.217661   0.123065  -1.769  0.07727 .  
stenose50-70%              0.015236   0.672137   0.023  0.98192    
stenose70-90%              0.450594   0.649635   0.694  0.48809    
stenose90-99%              0.396568   0.649397   0.611  0.54156    
stenose100% (Occlusion)    1.062603   0.825135   1.288  0.19813    
stenose50-99%              0.298635   1.206918   0.247  0.80462    
stenose70-99%             -0.047446   0.875053  -0.054  0.95677    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.438 on 966 degrees of freedom
Multiple R-squared:  0.0553,    Adjusted R-squared:  0.03672 
F-statistic: 2.976 on 19 and 966 DF,  p-value: 1.994e-05

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.147154 
Standard error............: 0.0314 
Odds ratio (effect size)..: 0.863 
Lower 95% CI..............: 0.812 
Upper 95% CI..............: 0.918 
T-value...................: -4.686438 
P-value...................: 3.178718e-06 
R^2.......................: 0.055301 
Adjusted r^2..............: 0.03672 
Sample size of AE DB......: 2388 
Sample size of model......: 986 
Missing data %............: 58.71022 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    SmokerCurrent + GFR_MDRD + CAD_history + Stroke_history + 
    Peripheral.interv, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes           SmokerCurrentyes                   GFR_MDRD  
                -2.624708                  -0.097253                  -0.220997                   0.182052                  -0.004191  
              CAD_history             Stroke_history          Peripheral.interv  
                -0.190862                   0.263254                  -0.199689  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.6531 -0.8703 -0.0006  0.8905  5.6434 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -2.038883   0.974633  -2.092  0.03673 * 
currentDF[, TRAIT]        -0.104738   0.057219  -1.830  0.06752 . 
Age                       -0.005291   0.006093  -0.868  0.38542   
Gendermale                -0.021403   0.109072  -0.196  0.84448   
Hypertension.compositeyes -0.192809   0.151642  -1.271  0.20390   
DiabetesStatusDiabetes    -0.042885   0.122174  -0.351  0.72566   
SmokerCurrentyes           0.131445   0.110742   1.187  0.23557   
Med.Statin.LLDyes         -0.052907   0.119575  -0.442  0.65827   
Med.all.antiplateletyes   -0.042817   0.168204  -0.255  0.79913   
GFR_MDRD                  -0.004739   0.002643  -1.793  0.07335 . 
BMI                       -0.011840   0.013579  -0.872  0.38349   
CAD_history               -0.158915   0.114224  -1.391  0.16450   
Stroke_history             0.283760   0.105814   2.682  0.00746 **
Peripheral.interv         -0.201818   0.133898  -1.507  0.13211   
stenose50-70%             -0.167862   0.687564  -0.244  0.80718   
stenose70-90%              0.277449   0.659841   0.420  0.67424   
stenose90-99%              0.218711   0.658975   0.332  0.74005   
stenose100% (Occlusion)    0.890786   0.861237   1.034  0.30128   
stenose50-99%              0.096182   1.222682   0.079  0.93732   
stenose70-99%             -0.592495   0.982979  -0.603  0.54683   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.458 on 870 degrees of freedom
Multiple R-squared:  0.03891,   Adjusted R-squared:  0.01792 
F-statistic: 1.854 on 19 and 870 DF,  p-value: 0.0145

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.104738 
Standard error............: 0.057219 
Odds ratio (effect size)..: 0.901 
Lower 95% CI..............: 0.805 
Upper 95% CI..............: 1.007 
T-value...................: -1.830485 
P-value...................: 0.06751939 
R^2.......................: 0.038911 
Adjusted r^2..............: 0.017922 
Sample size of AE DB......: 2388 
Sample size of model......: 890 
Missing data %............: 62.73032 

Analysis of IL6R_pg_ug_2015_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    DiabetesStatus + Med.Statin.LLD + GFR_MDRD + CAD_history + 
    Stroke_history + Peripheral.interv + stenose, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age   DiabetesStatusDiabetes        Med.Statin.LLDyes  
              -1.868663                 0.087162                -0.007644                -0.136979                -0.361825  
               GFR_MDRD              CAD_history           Stroke_history        Peripheral.interv            stenose50-70%  
              -0.002931                -0.119930                 0.134121                 0.264316                 0.578941  
          stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
               0.889927                 1.048488                 0.798484                 0.804088                -0.153072  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.3596 -0.5317  0.1550  0.6632  2.8575 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -1.614178   0.774058  -2.085   0.0373 *  
currentDF[, TRAIT]         0.089191   0.019929   4.475 8.54e-06 ***
Age                       -0.007346   0.004565  -1.609   0.1079    
Gendermale                -0.078883   0.080897  -0.975   0.3298    
Hypertension.compositeyes  0.016735   0.111904   0.150   0.8812    
DiabetesStatusDiabetes    -0.116262   0.088332  -1.316   0.1884    
SmokerCurrentyes           0.040877   0.081212   0.503   0.6148    
Med.Statin.LLDyes         -0.366933   0.089831  -4.085 4.78e-05 ***
Med.all.antiplateletyes    0.026960   0.121528   0.222   0.8245    
GFR_MDRD                  -0.002836   0.001955  -1.451   0.1471    
BMI                       -0.010516   0.010370  -1.014   0.3108    
CAD_history               -0.099551   0.082611  -1.205   0.2285    
Stroke_history             0.124764   0.077736   1.605   0.1088    
Peripheral.interv          0.252383   0.096699   2.610   0.0092 ** 
stenose50-70%              0.583432   0.583652   1.000   0.3177    
stenose70-90%              0.889630   0.566776   1.570   0.1168    
stenose90-99%              1.045135   0.566634   1.844   0.0654 .  
stenose100% (Occlusion)    0.794176   0.708635   1.121   0.2627    
stenose50-99%              0.788728   0.795545   0.991   0.3217    
stenose70-99%             -0.171338   0.728191  -0.235   0.8140    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.122 on 957 degrees of freedom
Multiple R-squared:  0.07669,   Adjusted R-squared:  0.05836 
F-statistic: 4.184 on 19 and 957 DF,  p-value: 5.997e-09

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0.089191 
Standard error............: 0.019929 
Odds ratio (effect size)..: 1.093 
Lower 95% CI..............: 1.051 
Upper 95% CI..............: 1.137 
T-value...................: 4.475492 
P-value...................: 8.54016e-06 
R^2.......................: 0.076694 
Adjusted r^2..............: 0.058363 
Sample size of AE DB......: 2388 
Sample size of model......: 977 
Missing data %............: 59.0871 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    DiabetesStatus + Med.Statin.LLD + GFR_MDRD + CAD_history + 
    Stroke_history + Peripheral.interv + stenose, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age   DiabetesStatusDiabetes        Med.Statin.LLDyes  
              -1.944179                 0.043465                -0.008985                -0.147927                -0.358007  
               GFR_MDRD              CAD_history           Stroke_history        Peripheral.interv            stenose50-70%  
              -0.002963                -0.118982                 0.153593                 0.256277                 0.609580  
          stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
               0.940725                 1.100813                 0.781036                 0.786098                -0.113250  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.4811 -0.5217  0.1442  0.6521  2.9130 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -1.861309   0.772888  -2.408  0.01622 *  
currentDF[, TRAIT]         0.042720   0.024761   1.725  0.08479 .  
Age                       -0.008717   0.004564  -1.910  0.05646 .  
Gendermale                -0.016853   0.080912  -0.208  0.83505    
Hypertension.compositeyes  0.031667   0.110631   0.286  0.77476    
DiabetesStatusDiabetes    -0.138529   0.088243  -1.570  0.11677    
SmokerCurrentyes           0.036806   0.081052   0.454  0.64985    
Med.Statin.LLDyes         -0.360922   0.088737  -4.067 5.14e-05 ***
Med.all.antiplateletyes    0.025253   0.121171   0.208  0.83496    
GFR_MDRD                  -0.002975   0.001966  -1.513  0.13049    
BMI                       -0.006163   0.010272  -0.600  0.54867    
CAD_history               -0.112193   0.082810  -1.355  0.17579    
Stroke_history             0.148217   0.077459   1.913  0.05598 .  
Peripheral.interv          0.250446   0.096792   2.587  0.00981 ** 
stenose50-70%              0.623885   0.585257   1.066  0.28669    
stenose70-90%              0.953389   0.568526   1.677  0.09388 .  
stenose90-99%              1.110673   0.568374   1.954  0.05097 .  
stenose100% (Occlusion)    0.795287   0.694155   1.146  0.25221    
stenose50-99%              0.779812   0.798436   0.977  0.32897    
stenose70-99%             -0.111279   0.730667  -0.152  0.87898    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.126 on 968 degrees of freedom
Multiple R-squared:  0.06282,   Adjusted R-squared:  0.04443 
F-statistic: 3.415 on 19 and 968 DF,  p-value: 1.128e-06

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: 0.04272 
Standard error............: 0.024761 
Odds ratio (effect size)..: 1.044 
Lower 95% CI..............: 0.994 
Upper 95% CI..............: 1.096 
T-value...................: 1.725318 
P-value...................: 0.08478925 
R^2.......................: 0.062822 
Adjusted r^2..............: 0.044427 
Sample size of AE DB......: 2388 
Sample size of model......: 988 
Missing data %............: 58.62647 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    DiabetesStatus + Med.Statin.LLD + GFR_MDRD + CAD_history + 
    Stroke_history + Peripheral.interv + stenose, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age   DiabetesStatusDiabetes        Med.Statin.LLDyes  
              -1.985548                 0.085060                -0.011047                -0.191927                -0.382204  
               GFR_MDRD              CAD_history           Stroke_history        Peripheral.interv            stenose50-70%  
              -0.003247                -0.151232                 0.134454                 0.264760                 0.670039  
          stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
               1.051640                 1.182766                 0.884950                 0.827384                -0.050675  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.4644 -0.5178  0.1193  0.6264  2.8616 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -1.720513   0.805252  -2.137   0.0329 *  
currentDF[, TRAIT]         0.084011   0.045007   1.867   0.0623 .  
Age                       -0.011289   0.004793  -2.355   0.0187 *  
Gendermale                -0.043677   0.084990  -0.514   0.6074    
Hypertension.compositeyes  0.029801   0.117703   0.253   0.8002    
DiabetesStatusDiabetes    -0.178220   0.095291  -1.870   0.0618 .  
SmokerCurrentyes           0.015091   0.086217   0.175   0.8611    
Med.Statin.LLDyes         -0.386904   0.093850  -4.123 4.11e-05 ***
Med.all.antiplateletyes    0.009582   0.132680   0.072   0.9424    
GFR_MDRD                  -0.003231   0.002111  -1.531   0.1262    
BMI                       -0.009544   0.010931  -0.873   0.3828    
CAD_history               -0.141443   0.088663  -1.595   0.1110    
Stroke_history             0.129125   0.082444   1.566   0.1177    
Peripheral.interv          0.256497   0.104752   2.449   0.0145 *  
stenose50-70%              0.665189   0.594432   1.119   0.2634    
stenose70-90%              1.048265   0.573848   1.827   0.0681 .  
stenose90-99%              1.176706   0.573255   2.053   0.0404 *  
stenose100% (Occlusion)    0.872861   0.717166   1.217   0.2239    
stenose50-99%              0.815870   0.804759   1.014   0.3110    
stenose70-99%             -0.040092   0.807060  -0.050   0.9604    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.135 on 869 degrees of freedom
Multiple R-squared:  0.06919,   Adjusted R-squared:  0.04884 
F-statistic:   3.4 on 19 and 869 DF,  p-value: 1.328e-06

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: 0.084011 
Standard error............: 0.045007 
Odds ratio (effect size)..: 1.088 
Lower 95% CI..............: 0.996 
Upper 95% CI..............: 1.188 
T-value...................: 1.8666 
P-value...................: 0.0622941 
R^2.......................: 0.069191 
Adjusted r^2..............: 0.04884 
Sample size of AE DB......: 2388 
Sample size of model......: 889 
Missing data %............: 62.77219 

Analysis of MCP1_pg_ug_2015_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Hypertension.composite + 
    Med.Statin.LLD + Stroke_history, data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes          Med.Statin.LLDyes             Stroke_history  
                  -0.8522                    -0.2802                    -0.2096                     0.1486  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.7571 -0.7488  0.0930  0.8590  3.4920 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -0.925358   0.842505  -1.098   0.2723  
currentDF[, TRAIT]        -0.026271   0.022722  -1.156   0.2479  
Age                       -0.001425   0.005170  -0.276   0.7830  
Gendermale                 0.097049   0.091595   1.060   0.2896  
Hypertension.compositeyes -0.269214   0.128299  -2.098   0.0361 *
DiabetesStatusDiabetes    -0.033010   0.100639  -0.328   0.7430  
SmokerCurrentyes          -0.009423   0.092386  -0.102   0.9188  
Med.Statin.LLDyes         -0.208582   0.102743  -2.030   0.0426 *
Med.all.antiplateletyes   -0.027788   0.138284  -0.201   0.8408  
GFR_MDRD                  -0.001712   0.002194  -0.780   0.4354  
BMI                       -0.005436   0.011533  -0.471   0.6375  
CAD_history               -0.061333   0.093962  -0.653   0.5141  
Stroke_history             0.140095   0.088863   1.577   0.1152  
Peripheral.interv          0.017046   0.109534   0.156   0.8764  
stenose50-70%              0.339845   0.610362   0.557   0.5778  
stenose70-90%              0.471059   0.589413   0.799   0.4244  
stenose90-99%              0.291657   0.589271   0.495   0.6207  
stenose100% (Occlusion)   -0.477681   0.770245  -0.620   0.5353  
stenose50-99%              0.769852   0.878037   0.877   0.3808  
stenose70-99%              0.638329   0.793977   0.804   0.4216  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.306 on 996 degrees of freedom
Multiple R-squared:  0.02457,   Adjusted R-squared:  0.005962 
F-statistic:  1.32 on 19 and 996 DF,  p-value: 0.1609

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: -0.026271 
Standard error............: 0.022722 
Odds ratio (effect size)..: 0.974 
Lower 95% CI..............: 0.932 
Upper 95% CI..............: 1.018 
T-value...................: -1.156187 
P-value...................: 0.2478818 
R^2.......................: 0.02457 
Adjusted r^2..............: 0.005962 
Sample size of AE DB......: 2388 
Sample size of model......: 1016 
Missing data %............: 57.45394 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    Med.Statin.LLD + Stroke_history, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes          Med.Statin.LLDyes             Stroke_history  
                 -0.85457                   -0.08447                   -0.26329                   -0.22108                    0.12716  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.7696 -0.7530  0.0762  0.8597  3.5363 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -0.8994588  0.8361173  -1.076  0.28229   
currentDF[, TRAIT]        -0.0835184  0.0278491  -2.999  0.00278 **
Age                       -0.0026486  0.0051357  -0.516  0.60617   
Gendermale                 0.0664656  0.0912138   0.729  0.46637   
Hypertension.compositeyes -0.2411143  0.1260696  -1.913  0.05609 . 
DiabetesStatusDiabetes    -0.0680869  0.0999108  -0.681  0.49573   
SmokerCurrentyes          -0.0088623  0.0916328  -0.097  0.92297   
Med.Statin.LLDyes         -0.2229455  0.1008775  -2.210  0.02733 * 
Med.all.antiplateletyes   -0.0224478  0.1370340  -0.164  0.86991   
GFR_MDRD                  -0.0009749  0.0021921  -0.445  0.65659   
BMI                       -0.0052354  0.0113634  -0.461  0.64510   
CAD_history               -0.0618465  0.0935719  -0.661  0.50879   
Stroke_history             0.1220969  0.0880165   1.387  0.16569   
Peripheral.interv          0.0617444  0.1089423   0.567  0.57100   
stenose50-70%              0.3690008  0.6080728   0.607  0.54410   
stenose70-90%              0.5043705  0.5875537   0.858  0.39086   
stenose90-99%              0.3427729  0.5874487   0.583  0.55969   
stenose100% (Occlusion)   -0.2187628  0.7464469  -0.293  0.76953   
stenose50-99%              0.8500869  0.8754372   0.971  0.33176   
stenose70-99%              0.7158192  0.7916197   0.904  0.36608   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.302 on 1007 degrees of freedom
Multiple R-squared:  0.03067,   Adjusted R-squared:  0.01238 
F-statistic: 1.677 on 19 and 1007 DF,  p-value: 0.03435

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.083518 
Standard error............: 0.027849 
Odds ratio (effect size)..: 0.92 
Lower 95% CI..............: 0.871 
Upper 95% CI..............: 0.971 
T-value...................: -2.998965 
P-value...................: 0.002775574 
R^2.......................: 0.030672 
Adjusted r^2..............: 0.012383 
Sample size of AE DB......: 2388 
Sample size of model......: 1027 
Missing data %............: 56.9933 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    Med.Statin.LLD + Stroke_history, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes          Med.Statin.LLDyes             Stroke_history  
                  -0.5912                    -0.1803                    -0.2543                    -0.2075                     0.1558  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.6053 -0.7439  0.0709  0.8139  3.6085 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -0.463146   0.873520  -0.530 0.596099    
currentDF[, TRAIT]        -0.176119   0.050659  -3.477 0.000532 ***
Age                       -0.001246   0.005407  -0.230 0.817776    
Gendermale                 0.138756   0.096023   1.445 0.148796    
Hypertension.compositeyes -0.245848   0.135031  -1.821 0.068985 .  
DiabetesStatusDiabetes    -0.059439   0.108067  -0.550 0.582439    
SmokerCurrentyes          -0.005477   0.097786  -0.056 0.955347    
Med.Statin.LLDyes         -0.205808   0.107190  -1.920 0.055167 .  
Med.all.antiplateletyes    0.042184   0.150129   0.281 0.778788    
GFR_MDRD                  -0.002446   0.002350  -1.041 0.298184    
BMI                       -0.008293   0.012091  -0.686 0.492954    
CAD_history               -0.061503   0.100738  -0.611 0.541669    
Stroke_history             0.144423   0.093922   1.538 0.124475    
Peripheral.interv          0.016866   0.118344   0.143 0.886704    
stenose50-70%              0.147602   0.621101   0.238 0.812209    
stenose70-90%              0.318132   0.595921   0.534 0.593576    
stenose90-99%              0.169478   0.595273   0.285 0.775934    
stenose100% (Occlusion)   -0.528418   0.778005  -0.679 0.497187    
stenose50-99%              0.810463   0.886127   0.915 0.360638    
stenose70-99%              0.234863   0.888016   0.264 0.791470    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.318 on 906 degrees of freedom
Multiple R-squared:  0.03654,   Adjusted R-squared:  0.01634 
F-statistic: 1.809 on 19 and 906 DF,  p-value: 0.01817

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.176119 
Standard error............: 0.050659 
Odds ratio (effect size)..: 0.839 
Lower 95% CI..............: 0.759 
Upper 95% CI..............: 0.926 
T-value...................: -3.476584 
P-value...................: 0.0005319754 
R^2.......................: 0.036541 
Adjusted r^2..............: 0.016336 
Sample size of AE DB......: 2388 
Sample size of model......: 926 
Missing data %............: 61.22278 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    Stroke_history, family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes             Stroke_history  
                  0.06077                    0.42883                   -0.31558  

Degrees of Freedom: 412 Total (i.e. Null);  410 Residual
Null Deviance:      561.6 
Residual Deviance: 557.3    AIC: 563.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9040  -1.2441   0.8325   1.0410   1.5634  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)
(Intercept)                0.211560   2.100615   0.101    0.920
currentDF[, PROTEIN]       0.142733   0.098280   1.452    0.146
Age                       -0.009077   0.014097  -0.644    0.520
Gendermale                -0.228810   0.242795  -0.942    0.346
Hypertension.compositeyes  0.506219   0.309468   1.636    0.102
DiabetesStatusDiabetes    -0.355035   0.267598  -1.327    0.185
SmokerCurrentyes          -0.078917   0.229881  -0.343    0.731
Med.Statin.LLDyes         -0.145398   0.239118  -0.608    0.543
Med.all.antiplateletyes    0.309251   0.366705   0.843    0.399
GFR_MDRD                  -0.010044   0.006340  -1.584    0.113
BMI                       -0.020982   0.029020  -0.723    0.470
CAD_history                0.061271   0.233414   0.262    0.793
Stroke_history            -0.329851   0.219902  -1.500    0.134
Peripheral.interv         -0.250407   0.260258  -0.962    0.336
stenose50-70%              1.444119   1.377917   1.048    0.295
stenose70-90%              1.402594   1.268968   1.105    0.269
stenose90-99%              1.071288   1.262660   0.848    0.396
stenose100% (Occlusion)    1.414209   1.589178   0.890    0.374

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 561.62  on 412  degrees of freedom
Residual deviance: 544.61  on 395  degrees of freedom
AIC: 580.61

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.142733 
Standard error............: 0.09828 
Odds ratio (effect size)..: 1.153 
Lower 95% CI..............: 0.951 
Upper 95% CI..............: 1.398 
Z-value...................: 1.452309 
P-value...................: 0.1464158 
Hosmer and Lemeshow r^2...: 0.030295 
Cox and Snell r^2.........: 0.04036 
Nagelkerke's pseudo r^2...: 0.054298 
Sample size of AE DB......: 2388 
Sample size of model......: 413 
Missing data %............: 82.70519 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)         SmokerCurrentyes            stenose50-70%            stenose70-90%            stenose90-99%  
              1.642e+01                4.689e-01                3.571e-09               -1.515e+01               -1.547e+01  
stenose100% (Occlusion)  
             -1.512e+01  

Degrees of Freedom: 411 Total (i.e. Null);  406 Residual
Null Deviance:      427.4 
Residual Deviance: 413.6    AIC: 425.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.12610   0.00028   0.62576   0.74886   1.10870  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.871e+01  2.248e+03   0.008   0.9934  
currentDF[, PROTEIN]       1.060e-01  1.182e-01   0.897   0.3696  
Age                       -1.050e-02  1.724e-02  -0.609   0.5426  
Gendermale                -4.748e-02  2.947e-01  -0.161   0.8720  
Hypertension.compositeyes  4.265e-01  3.623e-01   1.177   0.2391  
DiabetesStatusDiabetes     2.622e-01  3.339e-01   0.785   0.4323  
SmokerCurrentyes           4.934e-01  2.862e-01   1.724   0.0847 .
Med.Statin.LLDyes         -1.066e-01  2.892e-01  -0.368   0.7125  
Med.all.antiplateletyes    4.696e-01  4.214e-01   1.115   0.2651  
GFR_MDRD                  -1.124e-02  7.906e-03  -1.421   0.1553  
BMI                       -4.181e-02  3.521e-02  -1.188   0.2350  
CAD_history                8.257e-02  2.827e-01   0.292   0.7702  
Stroke_history             1.151e-01  2.726e-01   0.422   0.6729  
Peripheral.interv         -4.408e-01  3.013e-01  -1.463   0.1435  
stenose50-70%              2.979e-01  2.462e+03   0.000   0.9999  
stenose70-90%             -1.590e+01  2.248e+03  -0.007   0.9944  
stenose90-99%             -1.632e+01  2.248e+03  -0.007   0.9942  
stenose100% (Occlusion)   -1.561e+01  2.248e+03  -0.007   0.9945  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 427.39  on 411  degrees of freedom
Residual deviance: 404.07  on 394  degrees of freedom
AIC: 440.07

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.106009 
Standard error............: 0.118157 
Odds ratio (effect size)..: 1.112 
Lower 95% CI..............: 0.882 
Upper 95% CI..............: 1.402 
Z-value...................: 0.897189 
P-value...................: 0.3696183 
Hosmer and Lemeshow r^2...: 0.054567 
Cox and Snell r^2.........: 0.055034 
Nagelkerke's pseudo r^2...: 0.085243 
Sample size of AE DB......: 2388 
Sample size of model......: 412 
Missing data %............: 82.74707 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite + 
    Stroke_history, family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)                 Gendermale  Hypertension.compositeyes             Stroke_history  
                   0.1829                     0.6076                     0.7569                     0.7206  

Degrees of Freedom: 412 Total (i.e. Null);  409 Residual
Null Deviance:      403.2 
Residual Deviance: 387.6    AIC: 395.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3279   0.3992   0.5515   0.6787   1.2810  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.522e+01  1.375e+03   0.011   0.9912  
currentDF[, PROTEIN]       2.553e-02  1.198e-01   0.213   0.8313  
Age                       -1.085e-02  1.794e-02  -0.605   0.5453  
Gendermale                 6.661e-01  2.899e-01   2.298   0.0216 *
Hypertension.compositeyes  8.268e-01  3.505e-01   2.359   0.0183 *
DiabetesStatusDiabetes    -1.988e-01  3.316e-01  -0.599   0.5489  
SmokerCurrentyes           3.269e-01  2.994e-01   1.092   0.2750  
Med.Statin.LLDyes         -1.057e-01  3.071e-01  -0.344   0.7307  
Med.all.antiplateletyes    9.866e-02  4.636e-01   0.213   0.8315  
GFR_MDRD                  -7.000e-03  8.346e-03  -0.839   0.4016  
BMI                        1.383e-02  3.676e-02   0.376   0.7067  
CAD_history                1.454e-02  2.982e-01   0.049   0.9611  
Stroke_history             6.934e-01  3.085e-01   2.248   0.0246 *
Peripheral.interv         -3.647e-01  3.125e-01  -1.167   0.2432  
stenose50-70%             -1.505e+01  1.375e+03  -0.011   0.9913  
stenose70-90%             -1.426e+01  1.375e+03  -0.010   0.9917  
stenose90-99%             -1.446e+01  1.375e+03  -0.011   0.9916  
stenose100% (Occlusion)    9.090e-01  1.736e+03   0.001   0.9996  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 403.15  on 412  degrees of freedom
Residual deviance: 377.76  on 395  degrees of freedom
AIC: 413.76

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.025535 
Standard error............: 0.119833 
Odds ratio (effect size)..: 1.026 
Lower 95% CI..............: 0.811 
Upper 95% CI..............: 1.297 
Z-value...................: 0.213092 
P-value...................: 0.8312552 
Hosmer and Lemeshow r^2...: 0.062996 
Cox and Snell r^2.........: 0.059642 
Nagelkerke's pseudo r^2...: 0.095695 
Sample size of AE DB......: 2388 
Sample size of model......: 413 
Missing data %............: 82.70519 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite + 
    DiabetesStatus + Med.all.antiplatelet, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)                 Gendermale  Hypertension.compositeyes     DiabetesStatusDiabetes    Med.all.antiplateletyes  
                   1.0660                     0.5303                     0.5860                    -0.5228                    -0.7202  

Degrees of Freedom: 412 Total (i.e. Null);  408 Residual
Null Deviance:      454.9 
Residual Deviance: 441.3    AIC: 451.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0621   0.3753   0.6288   0.7913   1.2205  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -1.145802   2.323801  -0.493   0.6220  
currentDF[, PROTEIN]      -0.060240   0.109513  -0.550   0.5823  
Age                        0.013977   0.015971   0.875   0.3815  
Gendermale                 0.549378   0.264555   2.077   0.0378 *
Hypertension.compositeyes  0.485556   0.335458   1.447   0.1478  
DiabetesStatusDiabetes    -0.635087   0.295408  -2.150   0.0316 *
SmokerCurrentyes           0.155838   0.269384   0.578   0.5629  
Med.Statin.LLDyes          0.073143   0.275737   0.265   0.7908  
Med.all.antiplateletyes   -0.754014   0.523796  -1.440   0.1500  
GFR_MDRD                  -0.005491   0.007373  -0.745   0.4565  
BMI                        0.026724   0.032835   0.814   0.4157  
CAD_history                0.215890   0.279275   0.773   0.4395  
Stroke_history             0.030560   0.255858   0.119   0.9049  
Peripheral.interv          0.254194   0.314189   0.809   0.4185  
stenose50-70%              1.153696   1.419822   0.813   0.4165  
stenose70-90%              1.043858   1.278701   0.816   0.4143  
stenose90-99%              1.146267   1.274104   0.900   0.3683  
stenose100% (Occlusion)    1.034588   1.741930   0.594   0.5526  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 454.92  on 412  degrees of freedom
Residual deviance: 435.57  on 395  degrees of freedom
AIC: 471.57

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: IPH 
Effect size...............: -0.06024 
Standard error............: 0.109513 
Odds ratio (effect size)..: 0.942 
Lower 95% CI..............: 0.76 
Upper 95% CI..............: 1.167 
Z-value...................: -0.550072 
P-value...................: 0.58227 
Hosmer and Lemeshow r^2...: 0.042528 
Cox and Snell r^2.........: 0.045764 
Nagelkerke's pseudo r^2...: 0.068548 
Sample size of AE DB......: 2388 
Sample size of model......: 413 
Missing data %............: 82.70519 

Analysis of MCP1_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    DiabetesStatus + GFR_MDRD + Stroke_history + Peripheral.interv, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
           (Intercept)    currentDF[, PROTEIN]  DiabetesStatusDiabetes                GFR_MDRD          Stroke_history  
                2.3756                 -0.2319                 -0.4694                 -0.0093                 -0.3938  
     Peripheral.interv  
               -0.3572  

Degrees of Freedom: 506 Total (i.e. Null);  501 Residual
Null Deviance:      687.8 
Residual Deviance: 670.7    AIC: 682.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8298  -1.2494   0.8377   1.0142   1.5091  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)  
(Intercept)                0.071917   1.970396   0.036   0.9709  
currentDF[, PROTEIN]      -0.216531   0.113735  -1.904   0.0569 .
Age                        0.005647   0.012345   0.457   0.6474  
Gendermale                -0.098242   0.214322  -0.458   0.6467  
Hypertension.compositeyes  0.390757   0.278309   1.404   0.1603  
DiabetesStatusDiabetes    -0.521749   0.236227  -2.209   0.0272 *
SmokerCurrentyes           0.164273   0.204971   0.801   0.4229  
Med.Statin.LLDyes         -0.113143   0.218309  -0.518   0.6043  
Med.all.antiplateletyes    0.165116   0.336537   0.491   0.6237  
GFR_MDRD                  -0.008601   0.005339  -1.611   0.1072  
BMI                        0.005842   0.025430   0.230   0.8183  
CAD_history               -0.010525   0.211439  -0.050   0.9603  
Stroke_history            -0.406136   0.197879  -2.052   0.0401 *
Peripheral.interv         -0.334416   0.246904  -1.354   0.1756  
stenose50-70%              1.282607   1.371998   0.935   0.3499  
stenose70-90%              1.447780   1.284020   1.128   0.2595  
stenose90-99%              1.153776   1.279820   0.902   0.3673  
stenose100% (Occlusion)    1.265528   1.609157   0.786   0.4316  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 687.85  on 506  degrees of freedom
Residual deviance: 664.87  on 489  degrees of freedom
AIC: 700.87

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.216531 
Standard error............: 0.113735 
Odds ratio (effect size)..: 0.805 
Lower 95% CI..............: 0.644 
Upper 95% CI..............: 1.006 
Z-value...................: -1.90382 
P-value...................: 0.05693357 
Hosmer and Lemeshow r^2...: 0.033411 
Cox and Snell r^2.........: 0.044317 
Nagelkerke's pseudo r^2...: 0.059687 
Sample size of AE DB......: 2388 
Sample size of model......: 507 
Missing data %............: 78.76884 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerCurrent + Med.all.antiplatelet + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]         SmokerCurrentyes  Med.all.antiplateletyes        Peripheral.interv  
                18.6959                  -0.7585                   0.5419                   0.6881                  -0.5865  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
               -13.1027                 -14.0946                 -14.5925                 -14.1993  

Degrees of Freedom: 504 Total (i.e. Null);  496 Residual
Null Deviance:      499.8 
Residual Deviance: 456.3    AIC: 474.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5468   0.3210   0.5145   0.6873   1.3907  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                19.208916 778.940414   0.025   0.9803    
currentDF[, PROTEIN]       -0.747348   0.160259  -4.663 3.11e-06 ***
Age                        -0.002509   0.015925  -0.158   0.8748    
Gendermale                 -0.101596   0.281344  -0.361   0.7180    
Hypertension.compositeyes   0.242031   0.345102   0.701   0.4831    
DiabetesStatusDiabetes      0.295815   0.320385   0.923   0.3558    
SmokerCurrentyes            0.563563   0.272956   2.065   0.0390 *  
Med.Statin.LLDyes           0.035280   0.270331   0.131   0.8962    
Med.all.antiplateletyes     0.687607   0.396855   1.733   0.0832 .  
GFR_MDRD                   -0.002168   0.006966  -0.311   0.7556    
BMI                        -0.019309   0.034070  -0.567   0.5709    
CAD_history                 0.162940   0.269011   0.606   0.5447    
Stroke_history              0.118618   0.258214   0.459   0.6460    
Peripheral.interv          -0.645900   0.299607  -2.156   0.0311 *  
stenose50-70%             -13.110963 778.938449  -0.017   0.9866    
stenose70-90%             -14.120405 778.937755  -0.018   0.9855    
stenose90-99%             -14.635370 778.937743  -0.019   0.9850    
stenose100% (Occlusion)   -14.102429 778.938684  -0.018   0.9856    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 499.81  on 504  degrees of freedom
Residual deviance: 453.59  on 487  degrees of freedom
AIC: 489.59

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.747348 
Standard error............: 0.160259 
Odds ratio (effect size)..: 0.474 
Lower 95% CI..............: 0.346 
Upper 95% CI..............: 0.648 
Z-value...................: -4.663368 
P-value...................: 3.110754e-06 
Hosmer and Lemeshow r^2...: 0.092477 
Cox and Snell r^2.........: 0.087463 
Nagelkerke's pseudo r^2...: 0.139202 
Sample size of AE DB......: 2388 
Sample size of model......: 505 
Missing data %............: 78.8526 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Hypertension.composite + SmokerCurrent + Stroke_history, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]                 Gendermale  Hypertension.compositeyes           SmokerCurrentyes  
                  -3.3934                     0.7741                     0.5974                     0.7455                     0.3775  
           Stroke_history  
                   0.7308  

Degrees of Freedom: 506 Total (i.e. Null);  501 Residual
Null Deviance:      495 
Residual Deviance: 444.7    AIC: 456.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5589   0.2924   0.4739   0.6631   1.5573  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                8.460e+00  8.131e+02   0.010   0.9917    
currentDF[, PROTEIN]       7.856e-01  1.516e-01   5.183 2.18e-07 ***
Age                        3.727e-03  1.615e-02   0.231   0.8175    
Gendermale                 6.253e-01  2.612e-01   2.394   0.0167 *  
Hypertension.compositeyes  7.570e-01  3.445e-01   2.198   0.0280 *  
DiabetesStatusDiabetes    -2.620e-01  3.015e-01  -0.869   0.3848    
SmokerCurrentyes           3.903e-01  2.746e-01   1.422   0.1552    
Med.Statin.LLDyes         -6.708e-02  2.965e-01  -0.226   0.8210    
Med.all.antiplateletyes    1.913e-01  4.135e-01   0.463   0.6436    
GFR_MDRD                   1.764e-04  7.232e-03   0.024   0.9805    
BMI                        4.233e-02  3.303e-02   1.282   0.1999    
CAD_history               -2.006e-01  2.787e-01  -0.720   0.4716    
Stroke_history             7.218e-01  2.857e-01   2.526   0.0115 *  
Peripheral.interv         -1.379e-01  3.055e-01  -0.451   0.6516    
stenose50-70%             -1.418e+01  8.131e+02  -0.017   0.9861    
stenose70-90%             -1.306e+01  8.131e+02  -0.016   0.9872    
stenose90-99%             -1.339e+01  8.131e+02  -0.016   0.9869    
stenose100% (Occlusion)   -1.285e+01  8.131e+02  -0.016   0.9874    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 494.97  on 506  degrees of freedom
Residual deviance: 435.54  on 489  degrees of freedom
AIC: 471.54

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.785582 
Standard error............: 0.151558 
Odds ratio (effect size)..: 2.194 
Lower 95% CI..............: 1.63 
Upper 95% CI..............: 2.952 
Z-value...................: 5.183379 
P-value...................: 2.179012e-07 
Hosmer and Lemeshow r^2...: 0.120063 
Cox and Snell r^2.........: 0.110605 
Nagelkerke's pseudo r^2...: 0.177454 
Sample size of AE DB......: 2388 
Sample size of model......: 507 
Missing data %............: 78.76884 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    DiabetesStatus + BMI + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
           (Intercept)                     Age              Gendermale  DiabetesStatusDiabetes                     BMI  
              -2.11972                 0.02062                 0.76552                -0.51884                 0.05379  
     Peripheral.interv  
               0.43188  

Degrees of Freedom: 506 Total (i.e. Null);  501 Residual
Null Deviance:      557.2 
Residual Deviance: 535.1    AIC: 547.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1222   0.4422   0.6184   0.7615   1.5806  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)   
(Intercept)               -3.392924   2.168263  -1.565   0.1176   
currentDF[, PROTEIN]       0.131376   0.130297   1.008   0.3133   
Age                        0.014973   0.014227   1.052   0.2926   
Gendermale                 0.749485   0.233010   3.217   0.0013 **
Hypertension.compositeyes  0.270763   0.311744   0.869   0.3851   
DiabetesStatusDiabetes    -0.530889   0.264110  -2.010   0.0444 * 
SmokerCurrentyes           0.041490   0.239050   0.174   0.8622   
Med.Statin.LLDyes         -0.085328   0.258019  -0.331   0.7409   
Med.all.antiplateletyes   -0.097537   0.397652  -0.245   0.8062   
GFR_MDRD                  -0.005021   0.006267  -0.801   0.4230   
BMI                        0.051515   0.029392   1.753   0.0797 . 
CAD_history                0.187788   0.256176   0.733   0.4635   
Stroke_history             0.118708   0.232940   0.510   0.6103   
Peripheral.interv          0.418163   0.306767   1.363   0.1728   
stenose50-70%              1.262123   1.380939   0.914   0.3607   
stenose70-90%              1.184072   1.266946   0.935   0.3500   
stenose90-99%              1.383535   1.264436   1.094   0.2739   
stenose100% (Occlusion)    1.541946   1.722692   0.895   0.3707   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 557.22  on 506  degrees of freedom
Residual deviance: 529.66  on 489  degrees of freedom
AIC: 565.66

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: IPH 
Effect size...............: 0.131376 
Standard error............: 0.130297 
Odds ratio (effect size)..: 1.14 
Lower 95% CI..............: 0.883 
Upper 95% CI..............: 1.472 
Z-value...................: 1.008281 
P-value...................: 0.3133197 
Hosmer and Lemeshow r^2...: 0.049464 
Cox and Snell r^2.........: 0.052912 
Nagelkerke's pseudo r^2...: 0.079351 
Sample size of AE DB......: 2388 
Sample size of model......: 507 
Missing data %............: 78.76884 

Analysis of IL6_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age         SmokerCurrentyes              CAD_history  
               -1.44653                 -0.07084                  0.01994                  0.39936                  0.25473  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%  
               -1.00356                 -0.50547                 -0.26062                  0.82473                -13.93339  
          stenose70-99%  
               -1.52574  

Degrees of Freedom: 996 Total (i.e. Null);  986 Residual
Null Deviance:      1381 
Residual Deviance: 1349     AIC: 1371

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6142  -1.1333  -0.7968   1.1560   1.6718  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                -1.129798   1.325225  -0.853  0.39392   
currentDF[, PROTEIN]       -0.069823   0.045164  -1.546  0.12211   
Age                         0.016974   0.008100   2.095  0.03613 * 
Gendermale                 -0.133605   0.144147  -0.927  0.35400   
Hypertension.compositeyes   0.232025   0.202513   1.146  0.25191   
DiabetesStatusDiabetes     -0.174736   0.159528  -1.095  0.27337   
SmokerCurrentyes            0.414304   0.146715   2.824  0.00474 **
Med.Statin.LLDyes          -0.164594   0.159158  -1.034  0.30106   
Med.all.antiplateletyes    -0.223848   0.217190  -1.031  0.30270   
GFR_MDRD                   -0.001882   0.003492  -0.539  0.58999   
BMI                         0.012407   0.018096   0.686  0.49295   
CAD_history                 0.263345   0.149976   1.756  0.07910 . 
Stroke_history             -0.138179   0.140751  -0.982  0.32623   
Peripheral.interv          -0.183211   0.172892  -1.060  0.28929   
stenose50-70%              -0.937991   0.962569  -0.974  0.32983   
stenose70-90%              -0.484572   0.928921  -0.522  0.60191   
stenose90-99%              -0.235047   0.928443  -0.253  0.80014   
stenose100% (Occlusion)     0.820707   1.245138   0.659  0.50981   
stenose50-99%             -14.005575 368.450051  -0.038  0.96968   
stenose70-99%              -1.450206   1.253664  -1.157  0.24736   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1381.3  on 996  degrees of freedom
Residual deviance: 1340.1  on 977  degrees of freedom
AIC: 1380.1

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.069823 
Standard error............: 0.045164 
Odds ratio (effect size)..: 0.933 
Lower 95% CI..............: 0.854 
Upper 95% CI..............: 1.019 
Z-value...................: -1.545965 
P-value...................: 0.122113 
Hosmer and Lemeshow r^2...: 0.029786 
Cox and Snell r^2.........: 0.040427 
Nagelkerke's pseudo r^2...: 0.053918 
Sample size of AE DB......: 2388 
Sample size of model......: 997 
Missing data %............: 58.24958 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Hypertension.composite + SmokerCurrent + BMI + Stroke_history, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]  Hypertension.compositeyes           SmokerCurrentyes                        BMI  
                 -0.64730                   -0.19549                    0.33966                    0.44766                    0.03332  
           Stroke_history  
                  0.25010  

Degrees of Freedom: 999 Total (i.e. Null);  994 Residual
Null Deviance:      1017 
Residual Deviance: 993.2    AIC: 1005

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2738   0.4496   0.6096   0.7198   1.1160  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.232e+01  6.478e+02   0.019 0.984826    
currentDF[, PROTEIN]      -1.901e-01  5.643e-02  -3.370 0.000753 ***
Age                        8.224e-03  9.876e-03   0.833 0.405022    
Gendermale                -8.624e-02  1.781e-01  -0.484 0.628235    
Hypertension.compositeyes  3.110e-01  2.330e-01   1.335 0.181998    
DiabetesStatusDiabetes     2.110e-01  2.038e-01   1.035 0.300511    
SmokerCurrentyes           4.928e-01  1.863e-01   2.645 0.008165 ** 
Med.Statin.LLDyes         -1.679e-02  1.955e-01  -0.086 0.931556    
Med.all.antiplateletyes    1.321e-01  2.616e-01   0.505 0.613723    
GFR_MDRD                   4.699e-03  4.301e-03   1.092 0.274655    
BMI                        3.343e-02  2.363e-02   1.415 0.157050    
CAD_history                2.203e-01  1.882e-01   1.171 0.241696    
Stroke_history             2.401e-01  1.765e-01   1.360 0.173700    
Peripheral.interv         -1.730e-02  2.154e-01  -0.080 0.935980    
stenose50-70%             -1.361e+01  6.478e+02  -0.021 0.983245    
stenose70-90%             -1.401e+01  6.478e+02  -0.022 0.982741    
stenose90-99%             -1.406e+01  6.478e+02  -0.022 0.982686    
stenose100% (Occlusion)    5.521e-01  8.166e+02   0.001 0.999461    
stenose50-99%              1.725e-02  1.208e+03   0.000 0.999989    
stenose70-99%             -1.372e+01  6.478e+02  -0.021 0.983109    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1017.22  on 999  degrees of freedom
Residual deviance:  980.22  on 980  degrees of freedom
AIC: 1020.2

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.190149 
Standard error............: 0.056431 
Odds ratio (effect size)..: 0.827 
Lower 95% CI..............: 0.74 
Upper 95% CI..............: 0.924 
Z-value...................: -3.369571 
P-value...................: 0.000752854 
Hosmer and Lemeshow r^2...: 0.03637 
Cox and Snell r^2.........: 0.036321 
Nagelkerke's pseudo r^2...: 0.056893 
Sample size of AE DB......: 2388 
Sample size of model......: 1000 
Missing data %............: 58.12395 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale        Stroke_history     Peripheral.interv  
              1.5088                0.3120                0.8361                0.3705               -0.6128  

Degrees of Freedom: 999 Total (i.e. Null);  995 Residual
Null Deviance:      1165 
Residual Deviance: 1078     AIC: 1088

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2376  -1.0048   0.6046   0.8035   2.0522  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.416e+01  3.908e+02   0.036 0.971101    
currentDF[, PROTEIN]       3.150e-01  5.445e-02   5.785 7.25e-09 ***
Age                        9.863e-03  9.331e-03   1.057 0.290516    
Gendermale                 8.540e-01  1.612e-01   5.299 1.16e-07 ***
Hypertension.compositeyes  6.925e-02  2.349e-01   0.295 0.768118    
DiabetesStatusDiabetes    -1.188e-01  1.840e-01  -0.646 0.518436    
SmokerCurrentyes           9.721e-02  1.711e-01   0.568 0.569863    
Med.Statin.LLDyes         -1.762e-01  1.905e-01  -0.925 0.354945    
Med.all.antiplateletyes    6.857e-02  2.500e-01   0.274 0.783889    
GFR_MDRD                  -2.444e-04  4.099e-03  -0.060 0.952452    
BMI                        4.105e-03  2.044e-02   0.201 0.840876    
CAD_history                8.625e-02  1.753e-01   0.492 0.622789    
Stroke_history             3.758e-01  1.703e-01   2.207 0.027313 *  
Peripheral.interv         -6.179e-01  1.860e-01  -3.322 0.000895 ***
stenose50-70%             -1.354e+01  3.908e+02  -0.035 0.972374    
stenose70-90%             -1.348e+01  3.908e+02  -0.035 0.972476    
stenose90-99%             -1.333e+01  3.908e+02  -0.034 0.972791    
stenose100% (Occlusion)   -1.432e+01  3.908e+02  -0.037 0.970764    
stenose50-99%             -1.499e+01  3.908e+02  -0.038 0.969397    
stenose70-99%             -1.463e+01  3.908e+02  -0.037 0.970143    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1164.5  on 999  degrees of freedom
Residual deviance: 1066.6  on 980  degrees of freedom
AIC: 1106.6

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.314972 
Standard error............: 0.054447 
Odds ratio (effect size)..: 1.37 
Lower 95% CI..............: 1.232 
Upper 95% CI..............: 1.525 
Z-value...................: 5.78494 
P-value...................: 7.25384e-09 
Hosmer and Lemeshow r^2...: 0.084067 
Cox and Snell r^2.........: 0.093259 
Nagelkerke's pseudo r^2...: 0.135564 
Sample size of AE DB......: 2388 
Sample size of model......: 1000 
Missing data %............: 58.12395 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Med.Statin.LLD + 
    BMI + CAD_history + Stroke_history + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)               Gendermale        Med.Statin.LLDyes                      BMI              CAD_history  
                0.16028                  0.59271                 -0.25963                  0.02923                  0.30318  
         Stroke_history            stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
                0.25028                 -1.09743                 -0.97086                 -0.65159                 -0.80087  
          stenose50-99%            stenose70-99%  
              -15.27559                  0.59126  

Degrees of Freedom: 998 Total (i.e. Null);  987 Residual
Null Deviance:      1331 
Residual Deviance: 1288     AIC: 1312

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9361  -1.2697   0.8098   0.9793   1.4501  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 0.257533   1.493884   0.172   0.8631    
currentDF[, PROTEIN]        0.039778   0.046572   0.854   0.3930    
Age                         0.001667   0.008309   0.201   0.8410    
Gendermale                  0.639327   0.146514   4.364 1.28e-05 ***
Hypertension.compositeyes  -0.107679   0.207227  -0.520   0.6033    
DiabetesStatusDiabetes     -0.123508   0.163543  -0.755   0.4501    
SmokerCurrentyes            0.124348   0.151574   0.820   0.4120    
Med.Statin.LLDyes          -0.249362   0.167276  -1.491   0.1360    
Med.all.antiplateletyes     0.162196   0.221065   0.734   0.4631    
GFR_MDRD                   -0.004962   0.003614  -1.373   0.1698    
BMI                         0.033097   0.018698   1.770   0.0767 .  
CAD_history                 0.311877   0.157241   1.983   0.0473 *  
Stroke_history              0.230077   0.146495   1.571   0.1163    
Peripheral.interv           0.037229   0.178257   0.209   0.8346    
stenose50-70%              -1.012619   1.165333  -0.869   0.3849    
stenose70-90%              -0.905980   1.139839  -0.795   0.4267    
stenose90-99%              -0.591956   1.139857  -0.519   0.6035    
stenose100% (Occlusion)    -0.746506   1.357852  -0.550   0.5825    
stenose50-99%             -15.241325 376.943340  -0.040   0.9677    
stenose70-99%               0.596010   1.581074   0.377   0.7062    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1331.0  on 998  degrees of freedom
Residual deviance: 1283.3  on 979  degrees of freedom
AIC: 1323.3

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: 0.039778 
Standard error............: 0.046572 
Odds ratio (effect size)..: 1.041 
Lower 95% CI..............: 0.95 
Upper 95% CI..............: 1.14 
Z-value...................: 0.85412 
P-value...................: 0.3930386 
Hosmer and Lemeshow r^2...: 0.035859 
Cox and Snell r^2.........: 0.046653 
Nagelkerke's pseudo r^2...: 0.063374 
Sample size of AE DB......: 2388 
Sample size of model......: 999 
Missing data %............: 58.16583 

Analysis of IL6R_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Hypertension.composite + 
    DiabetesStatus + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)                        Age  Hypertension.compositeyes     DiabetesStatusDiabetes           SmokerCurrentyes  
                 -0.46167                    0.01611                    0.29114                   -0.24729                    0.32686  
              CAD_history              stenose50-70%              stenose70-90%              stenose90-99%    stenose100% (Occlusion)  
                  0.23621                   -1.57544                   -1.17320                   -0.93369                    0.08517  
            stenose50-99%              stenose70-99%  
                -15.69463                   -2.08381  

Degrees of Freedom: 999 Total (i.e. Null);  988 Residual
Null Deviance:      1385 
Residual Deviance: 1353     AIC: 1377

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.5698  -1.1312  -0.8315   1.1597   1.7348  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                 0.176748   1.498941   0.118   0.9061  
currentDF[, PROTEIN]       -0.019047   0.057409  -0.332   0.7401  
Age                         0.012980   0.008150   1.593   0.1113  
Gendermale                 -0.161581   0.144002  -1.122   0.2618  
Hypertension.compositeyes   0.296356   0.200256   1.480   0.1389  
DiabetesStatusDiabetes     -0.255738   0.159973  -1.599   0.1099  
SmokerCurrentyes            0.332542   0.145873   2.280   0.0226 *
Med.Statin.LLDyes          -0.112715   0.160946  -0.700   0.4837  
Med.all.antiplateletyes    -0.254266   0.219349  -1.159   0.2464  
GFR_MDRD                   -0.002446   0.003549  -0.689   0.4907  
BMI                         0.004983   0.018471   0.270   0.7873  
CAD_history                 0.257460   0.149355   1.724   0.0847 .
Stroke_history             -0.156422   0.139963  -1.118   0.2637  
Peripheral.interv          -0.196360   0.172913  -1.136   0.2561  
stenose50-70%              -1.456245   1.195518  -1.218   0.2232  
stenose70-90%              -1.097761   1.168294  -0.940   0.3474  
stenose90-99%              -0.843707   1.168572  -0.722   0.4703  
stenose100% (Occlusion)     0.133436   1.429817   0.093   0.9256  
stenose50-99%             -15.576906 435.896639  -0.036   0.9715  
stenose70-99%              -2.003314   1.437855  -1.393   0.1635  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1385.4  on 999  degrees of freedom
Residual deviance: 1346.1  on 980  degrees of freedom
AIC: 1386.1

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.019047 
Standard error............: 0.057409 
Odds ratio (effect size)..: 0.981 
Lower 95% CI..............: 0.877 
Upper 95% CI..............: 1.098 
Z-value...................: -0.331779 
P-value...................: 0.7400562 
Hosmer and Lemeshow r^2...: 0.028387 
Cox and Snell r^2.........: 0.038564 
Nagelkerke's pseudo r^2...: 0.051434 
Sample size of AE DB......: 2388 
Sample size of model......: 1000 
Missing data %............: 58.12395 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent + 
    CAD_history, family = binomial(link = "logit"), data = currentDF)

Coefficients:
     (Intercept)  SmokerCurrentyes       CAD_history  
          1.1267            0.4109            0.3192  

Degrees of Freedom: 1002 Total (i.e. Null);  1000 Residual
Null Deviance:      1019 
Residual Deviance: 1010     AIC: 1016

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1355   0.4944   0.6318   0.7184   0.9827  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.278e+01  7.194e+02   0.018   0.9858  
currentDF[, PROTEIN]      -1.366e-02  7.120e-02  -0.192   0.8478  
Age                        9.559e-03  9.927e-03   0.963   0.3356  
Gendermale                 5.029e-02  1.749e-01   0.287   0.7737  
Hypertension.compositeyes  2.868e-01  2.303e-01   1.245   0.2131  
DiabetesStatusDiabetes     1.921e-01  2.028e-01   0.947   0.3435  
SmokerCurrentyes           4.760e-01  1.852e-01   2.570   0.0102 *
Med.Statin.LLDyes          3.601e-02  1.950e-01   0.185   0.8535  
Med.all.antiplateletyes    2.336e-01  2.584e-01   0.904   0.3661  
GFR_MDRD                   4.087e-03  4.360e-03   0.937   0.3486  
BMI                        2.855e-02  2.336e-02   1.222   0.2218  
CAD_history                2.842e-01  1.881e-01   1.510   0.1310  
Stroke_history             2.282e-01  1.750e-01   1.304   0.1921  
Peripheral.interv          7.360e-02  2.159e-01   0.341   0.7332  
stenose50-70%             -1.364e+01  7.194e+02  -0.019   0.9849  
stenose70-90%             -1.408e+01  7.194e+02  -0.020   0.9844  
stenose90-99%             -1.407e+01  7.194e+02  -0.020   0.9844  
stenose100% (Occlusion)    3.431e-01  8.801e+02   0.000   0.9997  
stenose50-99%             -1.324e-01  1.020e+03   0.000   0.9999  
stenose70-99%             -1.371e+01  7.194e+02  -0.019   0.9848  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1018.60  on 1002  degrees of freedom
Residual deviance:  992.95  on  983  degrees of freedom
AIC: 1032.9

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.013662 
Standard error............: 0.071202 
Odds ratio (effect size)..: 0.986 
Lower 95% CI..............: 0.858 
Upper 95% CI..............: 1.134 
Z-value...................: -0.191877 
P-value...................: 0.8478382 
Hosmer and Lemeshow r^2...: 0.025181 
Cox and Snell r^2.........: 0.025248 
Nagelkerke's pseudo r^2...: 0.039586 
Sample size of AE DB......: 2388 
Sample size of model......: 1003 
Missing data %............: 57.99833 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale        Stroke_history     Peripheral.interv  
              0.6843                0.1101                0.7946                0.4350               -0.6186  

Degrees of Freedom: 1002 Total (i.e. Null);  998 Residual
Null Deviance:      1170 
Residual Deviance: 1121     AIC: 1131

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0766  -1.1267   0.6579   0.7991   1.8865  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.351e+01  4.411e+02   0.031  0.97557    
currentDF[, PROTEIN]       9.090e-02  6.341e-02   1.433  0.15172    
Age                        1.139e-02  9.258e-03   1.230  0.21871    
Gendermale                 8.404e-01  1.584e-01   5.306 1.12e-07 ***
Hypertension.compositeyes  1.693e-03  2.298e-01   0.007  0.99412    
DiabetesStatusDiabetes    -1.098e-01  1.802e-01  -0.609  0.54239    
SmokerCurrentyes           1.529e-01  1.676e-01   0.913  0.36148    
Med.Statin.LLDyes         -1.484e-01  1.900e-01  -0.781  0.43479    
Med.all.antiplateletyes    4.453e-02  2.492e-01   0.179  0.85821    
GFR_MDRD                  -5.368e-05  4.111e-03  -0.013  0.98958    
BMI                       -5.117e-03  2.076e-02  -0.246  0.80532    
CAD_history               -6.419e-02  1.697e-01  -0.378  0.70522    
Stroke_history             4.326e-01  1.662e-01   2.602  0.00927 ** 
Peripheral.interv         -5.951e-01  1.842e-01  -3.230  0.00124 ** 
stenose50-70%             -1.358e+01  4.411e+02  -0.031  0.97544    
stenose70-90%             -1.350e+01  4.411e+02  -0.031  0.97559    
stenose90-99%             -1.338e+01  4.411e+02  -0.030  0.97580    
stenose100% (Occlusion)   -1.416e+01  4.411e+02  -0.032  0.97438    
stenose50-99%             -1.588e+01  4.411e+02  -0.036  0.97129    
stenose70-99%             -1.469e+01  4.411e+02  -0.033  0.97343    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1170.4  on 1002  degrees of freedom
Residual deviance: 1106.1  on  983  degrees of freedom
AIC: 1146.1

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.090903 
Standard error............: 0.063414 
Odds ratio (effect size)..: 1.095 
Lower 95% CI..............: 0.967 
Upper 95% CI..............: 1.24 
Z-value...................: 1.433489 
P-value...................: 0.1517182 
Hosmer and Lemeshow r^2...: 0.054893 
Cox and Snell r^2.........: 0.062046 
Nagelkerke's pseudo r^2...: 0.090095 
Sample size of AE DB......: 2388 
Sample size of model......: 1003 
Missing data %............: 57.99833 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + GFR_MDRD + CAD_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale              GFR_MDRD           CAD_history  
            0.611874              0.124739              0.579682             -0.005085              0.226634  

Degrees of Freedom: 1000 Total (i.e. Null);  996 Residual
Null Deviance:      1337 
Residual Deviance: 1310     AIC: 1320

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9524  -1.2779   0.8266   0.9913   1.4676  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)                0.935176   1.526049   0.613   0.5400    
currentDF[, PROTEIN]       0.113747   0.058657   1.939   0.0525 .  
Age                       -0.001143   0.008376  -0.136   0.8915    
Gendermale                 0.610108   0.146043   4.178 2.95e-05 ***
Hypertension.compositeyes -0.106511   0.204153  -0.522   0.6019    
DiabetesStatusDiabetes    -0.149178   0.162470  -0.918   0.3585    
SmokerCurrentyes           0.117770   0.150419   0.783   0.4337    
Med.Statin.LLDyes         -0.155812   0.167235  -0.932   0.3515    
Med.all.antiplateletyes    0.059226   0.223727   0.265   0.7912    
GFR_MDRD                  -0.005411   0.003670  -1.474   0.1404    
BMI                        0.017802   0.018962   0.939   0.3478    
CAD_history                0.287268   0.155377   1.849   0.0645 .  
Stroke_history             0.144300   0.144433   0.999   0.3178    
Peripheral.interv          0.008765   0.178089   0.049   0.9607    
stenose50-70%             -0.738454   1.209037  -0.611   0.5413    
stenose70-90%             -0.767764   1.183606  -0.649   0.5166    
stenose90-99%             -0.518359   1.184171  -0.438   0.6616    
stenose100% (Occlusion)   -0.611519   1.393871  -0.439   0.6609    
stenose50-99%             -1.116618   1.557598  -0.717   0.4734    
stenose70-99%              0.867308   1.609921   0.539   0.5901    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1336.7  on 1000  degrees of freedom
Residual deviance: 1298.9  on  981  degrees of freedom
AIC: 1338.9

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: 0.113747 
Standard error............: 0.058657 
Odds ratio (effect size)..: 1.12 
Lower 95% CI..............: 0.999 
Upper 95% CI..............: 1.257 
Z-value...................: 1.93919 
P-value...................: 0.05247816 
Hosmer and Lemeshow r^2...: 0.028276 
Cox and Snell r^2.........: 0.037054 
Nagelkerke's pseudo r^2...: 0.050282 
Sample size of AE DB......: 2388 
Sample size of model......: 1001 
Missing data %............: 58.08208 

Analysis of MCP1_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age         SmokerCurrentyes              CAD_history  
               -1.82542                 -0.36236                  0.02094                  0.40404                  0.23730  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%  
               -0.89655                 -0.37855                 -0.19294                  0.70403                -14.79800  
          stenose70-99%  
               -1.22893  

Degrees of Freedom: 1038 Total (i.e. Null);  1028 Residual
Null Deviance:      1439 
Residual Deviance: 1355     AIC: 1377

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1140  -1.0778  -0.6941   1.1204   1.9531  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                -1.525276   1.332747  -1.144  0.25243    
currentDF[, PROTEIN]       -0.363190   0.052973  -6.856 7.08e-12 ***
Age                         0.017999   0.008173   2.202  0.02764 *  
Gendermale                 -0.140301   0.144212  -0.973  0.33061    
Hypertension.compositeyes   0.225902   0.202442   1.116  0.26447    
DiabetesStatusDiabetes     -0.244282   0.160978  -1.517  0.12914    
SmokerCurrentyes            0.414717   0.147104   2.819  0.00481 ** 
Med.Statin.LLDyes          -0.181849   0.161109  -1.129  0.25901    
Med.all.antiplateletyes    -0.202162   0.218816  -0.924  0.35555    
GFR_MDRD                   -0.001691   0.003523  -0.480  0.63129    
BMI                         0.012186   0.018168   0.671  0.50237    
CAD_history                 0.260080   0.149984   1.734  0.08291 .  
Stroke_history             -0.113342   0.140901  -0.804  0.42116    
Peripheral.interv          -0.188752   0.172455  -1.094  0.27374    
stenose50-70%              -0.823566   0.967587  -0.851  0.39468    
stenose70-90%              -0.342758   0.932281  -0.368  0.71313    
stenose90-99%              -0.156801   0.931886  -0.168  0.86638    
stenose100% (Occlusion)     0.711485   1.256594   0.566  0.57126    
stenose50-99%             -14.778796 419.945743  -0.035  0.97193    
stenose70-99%              -1.153193   1.267900  -0.910  0.36307    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1439.2  on 1038  degrees of freedom
Residual deviance: 1346.1  on 1019  degrees of freedom
AIC: 1386.1

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.36319 
Standard error............: 0.052973 
Odds ratio (effect size)..: 0.695 
Lower 95% CI..............: 0.627 
Upper 95% CI..............: 0.772 
Z-value...................: -6.856102 
P-value...................: 7.076467e-12 
Hosmer and Lemeshow r^2...: 0.064659 
Cox and Snell r^2.........: 0.085669 
Nagelkerke's pseudo r^2...: 0.114268 
Sample size of AE DB......: 2388 
Sample size of model......: 1039 
Missing data %............: 56.49079 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerCurrent + BMI, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]      SmokerCurrentyes                   BMI  
            -0.01029              -0.19213               0.44318               0.03858  

Degrees of Freedom: 1041 Total (i.e. Null);  1038 Residual
Null Deviance:      1055 
Residual Deviance: 1036     AIC: 1044

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3056   0.4422   0.6185   0.7210   1.0417  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.324e+01  1.060e+03   0.012  0.99003   
currentDF[, PROTEIN]      -1.874e-01  6.331e-02  -2.961  0.00307 **
Age                        1.043e-02  9.729e-03   1.072  0.28352   
Gendermale                 2.184e-02  1.728e-01   0.126  0.89942   
Hypertension.compositeyes  2.349e-01  2.302e-01   1.020  0.30753   
DiabetesStatusDiabetes     1.277e-01  1.982e-01   0.644  0.51942   
SmokerCurrentyes           5.186e-01  1.837e-01   2.823  0.00475 **
Med.Statin.LLDyes          1.202e-02  1.924e-01   0.062  0.95021   
Med.all.antiplateletyes    1.550e-01  2.563e-01   0.605  0.54545   
GFR_MDRD                   5.536e-03  4.245e-03   1.304  0.19220   
BMI                        4.039e-02  2.311e-02   1.748  0.08050 . 
CAD_history                2.111e-01  1.839e-01   1.148  0.25100   
Stroke_history             2.243e-01  1.728e-01   1.298  0.19424   
Peripheral.interv          1.123e-01  2.127e-01   0.528  0.59749   
stenose50-70%             -1.453e+01  1.060e+03  -0.014  0.98906   
stenose70-90%             -1.499e+01  1.060e+03  -0.014  0.98872   
stenose90-99%             -1.505e+01  1.060e+03  -0.014  0.98867   
stenose100% (Occlusion)    2.360e-01  1.350e+03   0.000  0.99986   
stenose50-99%             -6.401e-03  1.588e+03   0.000  1.00000   
stenose70-99%             -1.448e+01  1.060e+03  -0.014  0.98910   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1055.5  on 1041  degrees of freedom
Residual deviance: 1018.8  on 1022  degrees of freedom
AIC: 1058.8

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.187427 
Standard error............: 0.063307 
Odds ratio (effect size)..: 0.829 
Lower 95% CI..............: 0.732 
Upper 95% CI..............: 0.939 
Z-value...................: -2.960631 
P-value...................: 0.003070098 
Hosmer and Lemeshow r^2...: 0.034718 
Cox and Snell r^2.........: 0.034555 
Nagelkerke's pseudo r^2...: 0.05426 
Sample size of AE DB......: 2388 
Sample size of model......: 1042 
Missing data %............: 56.36516 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]               Gendermale           Stroke_history        Peripheral.interv  
                14.0651                   0.1331                   0.8322                   0.4430                  -0.6200  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%  
               -13.6515                 -13.5243                 -13.3644                 -14.0689                 -15.9382  
          stenose70-99%  
               -14.7683  

Degrees of Freedom: 1041 Total (i.e. Null);  1031 Residual
Null Deviance:      1225 
Residual Deviance: 1154     AIC: 1176

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0991  -1.1400   0.6479   0.8098   1.7713  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.339e+01  3.919e+02   0.034 0.972738    
currentDF[, PROTEIN]       1.285e-01  5.518e-02   2.328 0.019925 *  
Age                        7.929e-03  8.973e-03   0.884 0.376898    
Gendermale                 8.698e-01  1.541e-01   5.643 1.67e-08 ***
Hypertension.compositeyes  4.466e-02  2.266e-01   0.197 0.843736    
DiabetesStatusDiabetes    -1.596e-01  1.756e-01  -0.909 0.363550    
SmokerCurrentyes           1.456e-01  1.637e-01   0.889 0.373964    
Med.Statin.LLDyes         -1.853e-01  1.862e-01  -0.995 0.319583    
Med.all.antiplateletyes    8.146e-02  2.423e-01   0.336 0.736740    
GFR_MDRD                  -5.399e-04  3.928e-03  -0.137 0.890678    
BMI                        4.117e-03  1.973e-02   0.209 0.834748    
CAD_history               -3.684e-02  1.661e-01  -0.222 0.824449    
Stroke_history             4.317e-01  1.633e-01   2.643 0.008213 ** 
Peripheral.interv         -5.934e-01  1.791e-01  -3.313 0.000923 ***
stenose50-70%             -1.358e+01  3.919e+02  -0.035 0.972348    
stenose70-90%             -1.346e+01  3.919e+02  -0.034 0.972607    
stenose90-99%             -1.331e+01  3.919e+02  -0.034 0.972907    
stenose100% (Occlusion)   -1.405e+01  3.919e+02  -0.036 0.971411    
stenose50-99%             -1.592e+01  3.919e+02  -0.041 0.967601    
stenose70-99%             -1.478e+01  3.919e+02  -0.038 0.969917    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1224.7  on 1041  degrees of freedom
Residual deviance: 1150.0  on 1022  degrees of freedom
AIC: 1190

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.128452 
Standard error............: 0.055183 
Odds ratio (effect size)..: 1.137 
Lower 95% CI..............: 1.021 
Upper 95% CI..............: 1.267 
Z-value...................: 2.327761 
P-value...................: 0.01992479 
Hosmer and Lemeshow r^2...: 0.060989 
Cox and Snell r^2.........: 0.069173 
Nagelkerke's pseudo r^2...: 0.100065 
Sample size of AE DB......: 2388 
Sample size of model......: 1042 
Missing data %............: 56.36516 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Med.Statin.LLD + BMI + CAD_history + Stroke_history, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale     Med.Statin.LLDyes                   BMI           CAD_history  
            -0.68313              -0.09602               0.54823              -0.26886               0.02693               0.26525  
      Stroke_history  
             0.23930  

Degrees of Freedom: 1039 Total (i.e. Null);  1033 Residual
Null Deviance:      1389 
Residual Deviance: 1358     AIC: 1372

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9553  -1.2805   0.8149   0.9916   1.5235  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                0.1754155  1.4836591   0.118   0.9059    
currentDF[, PROTEIN]      -0.0953309  0.0507963  -1.877   0.0606 .  
Age                        0.0002349  0.0081421   0.029   0.9770    
Gendermale                 0.6158358  0.1429276   4.309 1.64e-05 ***
Hypertension.compositeyes -0.1314574  0.2022681  -0.650   0.5157    
DiabetesStatusDiabetes    -0.1324652  0.1596057  -0.830   0.4066    
SmokerCurrentyes           0.1353568  0.1475791   0.917   0.3590    
Med.Statin.LLDyes         -0.2565402  0.1644513  -1.560   0.1188    
Med.all.antiplateletyes    0.1480708  0.2179980   0.679   0.4970    
GFR_MDRD                  -0.0049268  0.0035325  -1.395   0.1631    
BMI                        0.0315725  0.0183189   1.723   0.0848 .  
CAD_history                0.2686674  0.1520378   1.767   0.0772 .  
Stroke_history             0.2381253  0.1426755   1.669   0.0951 .  
Peripheral.interv          0.0540398  0.1736594   0.311   0.7557    
stenose50-70%             -0.9864070  1.1701913  -0.843   0.3993    
stenose70-90%             -0.8577626  1.1446041  -0.749   0.4536    
stenose90-99%             -0.6037415  1.1445931  -0.527   0.5979    
stenose100% (Occlusion)   -0.7476970  1.3616375  -0.549   0.5829    
stenose50-99%             -1.2390402  1.5273473  -0.811   0.4172    
stenose70-99%              0.6388947  1.5823481   0.404   0.6864    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1388.6  on 1039  degrees of freedom
Residual deviance: 1345.2  on 1020  degrees of freedom
AIC: 1385.2

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: -0.095331 
Standard error............: 0.050796 
Odds ratio (effect size)..: 0.909 
Lower 95% CI..............: 0.823 
Upper 95% CI..............: 1.004 
Z-value...................: -1.876731 
P-value...................: 0.06055499 
Hosmer and Lemeshow r^2...: 0.031257 
Cox and Snell r^2.........: 0.040876 
Nagelkerke's pseudo r^2...: 0.05547 
Sample size of AE DB......: 2388 
Sample size of model......: 1040 
Missing data %............: 56.44891 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD + 
    CAD_history, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]   Med.Statin.LLDyes         CAD_history  
           0.06578            -0.06829            -0.15943             0.17897  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.18012 -0.68054  0.01814  0.64545  3.12461 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                2.0235458  0.8716104   2.322   0.0207 *
currentDF[, TRAIT]        -0.0685416  0.0417359  -1.642   0.1012  
Age                       -0.0066245  0.0057643  -1.149   0.2510  
Gendermale                -0.1469518  0.1015109  -1.448   0.1484  
Hypertension.compositeyes  0.0462797  0.1352200   0.342   0.7323  
DiabetesStatusDiabetes    -0.0287032  0.1144777  -0.251   0.8021  
SmokerCurrentyes           0.0296470  0.0980327   0.302   0.7625  
Med.Statin.LLDyes         -0.1940609  0.1023977  -1.895   0.0587 .
Med.all.antiplateletyes   -0.2470769  0.1608439  -1.536   0.1252  
GFR_MDRD                   0.0005454  0.0025737   0.212   0.8323  
BMI                       -0.0165307  0.0123925  -1.334   0.1829  
CAD_history                0.2248719  0.1011374   2.223   0.0267 *
Stroke_history             0.0695885  0.0961747   0.724   0.4697  
Peripheral.interv         -0.0107614  0.1147908  -0.094   0.9254  
stenose50-70%             -0.8489816  0.6177818  -1.374   0.1700  
stenose70-90%             -0.8371936  0.5706953  -1.467   0.1431  
stenose90-99%             -0.8337094  0.5690414  -1.465   0.1436  
stenose100% (Occlusion)   -1.3089080  0.6810469  -1.922   0.0552 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9653 on 462 degrees of freedom
Multiple R-squared:  0.03962,   Adjusted R-squared:  0.004284 
F-statistic: 1.121 on 17 and 462 DF,  p-value: 0.3297

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: -0.068542 
Standard error............: 0.041736 
Odds ratio (effect size)..: 0.934 
Lower 95% CI..............: 0.86 
Upper 95% CI..............: 1.013 
T-value...................: -1.64227 
P-value...................: 0.1012144 
R^2.......................: 0.039622 
Adjusted r^2..............: 0.004284 
Sample size of AE DB......: 2388 
Sample size of model......: 480 
Missing data %............: 79.8995 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + Med.Statin.LLD + 
    BMI + CAD_history, data = currentDF)

Coefficients:
      (Intercept)         Gendermale  Med.Statin.LLDyes                BMI        CAD_history  
          0.62797           -0.14791           -0.17078           -0.01678            0.18459  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.10464 -0.69874  0.03157  0.67237  3.02975 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                2.0177233  0.8817683   2.288   0.0226 *
currentDF[, TRAIT]         0.0247937  0.0442872   0.560   0.5759  
Age                       -0.0062426  0.0059358  -1.052   0.2935  
Gendermale                -0.1471284  0.1039038  -1.416   0.1575  
Hypertension.compositeyes  0.0194356  0.1356093   0.143   0.8861  
DiabetesStatusDiabetes    -0.0182235  0.1157824  -0.157   0.8750  
SmokerCurrentyes           0.0278929  0.0989731   0.282   0.7782  
Med.Statin.LLDyes         -0.1848209  0.1040135  -1.777   0.0762 .
Med.all.antiplateletyes   -0.2376107  0.1622751  -1.464   0.1438  
GFR_MDRD                   0.0005086  0.0026001   0.196   0.8450  
BMI                       -0.0174703  0.0124984  -1.398   0.1628  
CAD_history                0.1915058  0.1021081   1.876   0.0614 .
Stroke_history             0.0570212  0.0971729   0.587   0.5576  
Peripheral.interv          0.0059065  0.1162497   0.051   0.9595  
stenose50-70%             -0.8228165  0.6236546  -1.319   0.1877  
stenose70-90%             -0.8299597  0.5757742  -1.441   0.1501  
stenose90-99%             -0.7914493  0.5736687  -1.380   0.1684  
stenose100% (Occlusion)   -1.2535737  0.6865446  -1.826   0.0685 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9735 on 459 degrees of freedom
Multiple R-squared:  0.03312,   Adjusted R-squared:  -0.002686 
F-statistic: 0.925 on 17 and 459 DF,  p-value: 0.5443

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: 0.024794 
Standard error............: 0.044287 
Odds ratio (effect size)..: 1.025 
Lower 95% CI..............: 0.94 
Upper 95% CI..............: 1.118 
T-value...................: 0.55984 
P-value...................: 0.5758615 
R^2.......................: 0.033124 
Adjusted r^2..............: -0.002686 
Sample size of AE DB......: 2388 
Sample size of model......: 477 
Missing data %............: 80.02513 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD + Med.all.antiplatelet + 
    CAD_history, data = currentDF)

Coefficients:
            (Intercept)        Med.Statin.LLDyes  Med.all.antiplateletyes              CAD_history  
                 0.2792                  -0.1532                  -0.2243                   0.1419  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.09803 -0.68750  0.01001  0.67790  3.09629 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                1.9561136  0.8855768   2.209   0.0277 *
currentDF[, TRAIT]        -0.0323443  0.0567070  -0.570   0.5687  
Age                       -0.0071652  0.0058979  -1.215   0.2250  
Gendermale                -0.1413460  0.1036164  -1.364   0.1732  
Hypertension.compositeyes  0.0500332  0.1385484   0.361   0.7182  
DiabetesStatusDiabetes    -0.0297531  0.1175472  -0.253   0.8003  
SmokerCurrentyes           0.0287580  0.0998963   0.288   0.7736  
Med.Statin.LLDyes         -0.1881625  0.1043566  -1.803   0.0720 .
Med.all.antiplateletyes   -0.2581952  0.1641560  -1.573   0.1164  
GFR_MDRD                   0.0006495  0.0026302   0.247   0.8051  
BMI                       -0.0144740  0.0127453  -1.136   0.2567  
CAD_history                0.1948838  0.1024046   1.903   0.0577 .
Stroke_history             0.0843472  0.0983333   0.858   0.3915  
Peripheral.interv          0.0036368  0.1168602   0.031   0.9752  
stenose50-70%             -0.7800714  0.6238008  -1.251   0.2118  
stenose70-90%             -0.8067160  0.5767651  -1.399   0.1626  
stenose90-99%             -0.7793910  0.5748524  -1.356   0.1758  
stenose100% (Occlusion)   -0.9176088  0.7281714  -1.260   0.2083  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9754 on 452 degrees of freedom
Multiple R-squared:  0.03081,   Adjusted R-squared:  -0.005638 
F-statistic: 0.8453 on 17 and 452 DF,  p-value: 0.64

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.032344 
Standard error............: 0.056707 
Odds ratio (effect size)..: 0.968 
Lower 95% CI..............: 0.866 
Upper 95% CI..............: 1.082 
T-value...................: -0.570376 
P-value...................: 0.5687063 
R^2.......................: 0.030814 
Adjusted r^2..............: -0.005638 
Sample size of AE DB......: 2388 
Sample size of model......: 470 
Missing data %............: 80.31826 

Analysis of MCP1_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + Med.Statin.LLD + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale  Hypertension.compositeyes  
                 0.371369                   0.098495                  -0.007782                   0.304434                  -0.224359  
        Med.Statin.LLDyes    Med.all.antiplateletyes  
                -0.214084                   0.325933  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.04992 -0.67471  0.01276  0.66488  2.76675 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.175752   0.858226   1.370  0.17131   
currentDF[, TRAIT]         0.091886   0.040985   2.242  0.02541 * 
Age                       -0.011585   0.005692  -2.035  0.04237 * 
Gendermale                 0.290308   0.098872   2.936  0.00348 **
Hypertension.compositeyes -0.233518   0.132648  -1.760  0.07895 . 
DiabetesStatusDiabetes    -0.121623   0.110871  -1.097  0.27318   
SmokerCurrentyes          -0.062330   0.095859  -0.650  0.51585   
Med.Statin.LLDyes         -0.220521   0.101730  -2.168  0.03066 * 
Med.all.antiplateletyes    0.301211   0.154422   1.951  0.05167 . 
GFR_MDRD                  -0.001676   0.002477  -0.677  0.49899   
BMI                       -0.012215   0.011885  -1.028  0.30455   
CAD_history                0.110540   0.099554   1.110  0.26739   
Stroke_history             0.115056   0.093287   1.233  0.21803   
Peripheral.interv         -0.150917   0.117008  -1.290  0.19772   
stenose50-70%             -0.280324   0.622960  -0.450  0.65292   
stenose70-90%             -0.054810   0.578053  -0.095  0.92450   
stenose90-99%             -0.042985   0.576571  -0.075  0.94060   
stenose100% (Occlusion)   -0.487945   0.729821  -0.669  0.50407   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9798 on 495 degrees of freedom
Multiple R-squared:  0.07266,   Adjusted R-squared:  0.04081 
F-statistic: 2.282 on 17 and 495 DF,  p-value: 0.002535

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.091886 
Standard error............: 0.040985 
Odds ratio (effect size)..: 1.096 
Lower 95% CI..............: 1.012 
Upper 95% CI..............: 1.188 
T-value...................: 2.241938 
P-value...................: 0.02540749 
R^2.......................: 0.072662 
Adjusted r^2..............: 0.040814 
Sample size of AE DB......: 2388 
Sample size of model......: 513 
Missing data %............: 78.51759 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + Med.Statin.LLD + Med.all.antiplatelet + 
    CAD_history, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale  Hypertension.compositeyes  
                  0.79555                   -0.18754                   -0.01327                    0.22987                   -0.22144  
        Med.Statin.LLDyes    Med.all.antiplateletyes                CAD_history  
                 -0.22763                    0.32134                    0.13508  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.82157 -0.67164 -0.01232  0.64680  2.51654 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.516767   0.843745   1.798  0.07284 .  
currentDF[, TRAIT]        -0.184221   0.041848  -4.402 1.32e-05 ***
Age                       -0.016216   0.005685  -2.853  0.00452 ** 
Gendermale                 0.227577   0.098012   2.322  0.02064 *  
Hypertension.compositeyes -0.217011   0.129139  -1.680  0.09351 .  
DiabetesStatusDiabetes    -0.111461   0.108990  -1.023  0.30697    
SmokerCurrentyes          -0.064436   0.093923  -0.686  0.49300    
Med.Statin.LLDyes         -0.226750   0.100142  -2.264  0.02399 *  
Med.all.antiplateletyes    0.292797   0.151249   1.936  0.05346 .  
GFR_MDRD                  -0.001337   0.002428  -0.551  0.58211    
BMI                       -0.011722   0.011633  -1.008  0.31411    
CAD_history                0.166154   0.097613   1.702  0.08935 .  
Stroke_history             0.113839   0.091470   1.245  0.21389    
Peripheral.interv         -0.131918   0.115289  -1.144  0.25308    
stenose50-70%             -0.246421   0.610039  -0.404  0.68643    
stenose70-90%             -0.055765   0.566182  -0.098  0.92158    
stenose90-99%             -0.067374   0.564421  -0.119  0.90503    
stenose100% (Occlusion)   -0.599452   0.714404  -0.839  0.40182    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9594 on 492 degrees of freedom
Multiple R-squared:  0.09563,   Adjusted R-squared:  0.06438 
F-statistic:  3.06 on 17 and 492 DF,  p-value: 3.937e-05

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.184221 
Standard error............: 0.041848 
Odds ratio (effect size)..: 0.832 
Lower 95% CI..............: 0.766 
Upper 95% CI..............: 0.903 
T-value...................: -4.402188 
P-value...................: 1.315371e-05 
R^2.......................: 0.095625 
Adjusted r^2..............: 0.064376 
Sample size of AE DB......: 2388 
Sample size of model......: 510 
Missing data %............: 78.64322 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Med.Statin.LLD + Med.all.antiplatelet, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age               Gendermale        Med.Statin.LLDyes  
                0.30680                 -0.09313                 -0.00927                  0.32989                 -0.22147  
Med.all.antiplateletyes  
                0.32424  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.08723 -0.66290 -0.00982  0.64415  2.61016 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.081798   0.865574   1.250  0.21197   
currentDF[, TRAIT]        -0.098755   0.054234  -1.821  0.06924 . 
Age                       -0.012056   0.005761  -2.093  0.03690 * 
Gendermale                 0.311368   0.100077   3.111  0.00197 **
Hypertension.compositeyes -0.171260   0.135231  -1.266  0.20597   
DiabetesStatusDiabetes    -0.094615   0.112880  -0.838  0.40234   
SmokerCurrentyes          -0.067259   0.097251  -0.692  0.48951   
Med.Statin.LLDyes         -0.226120   0.102933  -2.197  0.02851 * 
Med.all.antiplateletyes    0.297676   0.156807   1.898  0.05824 . 
GFR_MDRD                  -0.001178   0.002544  -0.463  0.64359   
BMI                       -0.009717   0.012040  -0.807  0.42001   
CAD_history                0.136143   0.100924   1.349  0.17798   
Stroke_history             0.120457   0.094866   1.270  0.20478   
Peripheral.interv         -0.133039   0.118703  -1.121  0.26294   
stenose50-70%             -0.359473   0.625133  -0.575  0.56553   
stenose70-90%             -0.053904   0.580427  -0.093  0.92605   
stenose90-99%             -0.062723   0.578764  -0.108  0.91374   
stenose100% (Occlusion)   -0.550773   0.732645  -0.752  0.45256   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9835 on 485 degrees of freedom
Multiple R-squared:  0.06824,   Adjusted R-squared:  0.03558 
F-statistic: 2.089 on 17 and 485 DF,  p-value: 0.006596

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.098755 
Standard error............: 0.054234 
Odds ratio (effect size)..: 0.906 
Lower 95% CI..............: 0.815 
Upper 95% CI..............: 1.008 
T-value...................: -1.820907 
P-value...................: 0.06923707 
R^2.......................: 0.068242 
Adjusted r^2..............: 0.035582 
Sample size of AE DB......: 2388 
Sample size of model......: 503 
Missing data %............: 78.93635 

Analysis of IL6_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + CAD_history + 
    Stroke_history + Peripheral.interv, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]         CAD_history      Stroke_history   Peripheral.interv  
           0.01886             0.09601            -0.14506             0.18452            -0.14721  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1909 -0.6764  0.0049  0.6426  3.4119 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.598104   0.634370   0.943  0.34600   
currentDF[, TRAIT]         0.093832   0.031754   2.955  0.00320 **
Age                       -0.003879   0.003906  -0.993  0.32090   
Gendermale                 0.009744   0.069831   0.140  0.88906   
Hypertension.compositeyes -0.100809   0.097138  -1.038  0.29962   
DiabetesStatusDiabetes    -0.008597   0.076764  -0.112  0.91086   
SmokerCurrentyes           0.064296   0.070471   0.912  0.36179   
Med.Statin.LLDyes         -0.064710   0.076835  -0.842  0.39988   
Med.all.antiplateletyes    0.011430   0.104312   0.110  0.91277   
GFR_MDRD                  -0.002656   0.001676  -1.585  0.11323   
BMI                       -0.008334   0.008727  -0.955  0.33981   
CAD_history               -0.108752   0.072191  -1.506  0.13228   
Stroke_history             0.188239   0.067608   2.784  0.00547 **
Peripheral.interv         -0.149582   0.083267  -1.796  0.07274 . 
stenose50-70%             -0.037014   0.459362  -0.081  0.93579   
stenose70-90%              0.226650   0.444258   0.510  0.61004   
stenose90-99%              0.173795   0.444035   0.391  0.69559   
stenose100% (Occlusion)    0.562595   0.564475   0.997  0.31917   
stenose50-99%              0.021481   0.824910   0.026  0.97923   
stenose70-99%             -0.186502   0.598315  -0.312  0.75533   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.984 on 976 degrees of freedom
Multiple R-squared:  0.04085,   Adjusted R-squared:  0.02218 
F-statistic: 2.188 on 19 and 976 DF,  p-value: 0.002371

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.093832 
Standard error............: 0.031754 
Odds ratio (effect size)..: 1.098 
Lower 95% CI..............: 1.032 
Upper 95% CI..............: 1.169 
T-value...................: 2.954983 
P-value...................: 0.003201987 
R^2.......................: 0.040848 
Adjusted r^2..............: 0.022176 
Sample size of AE DB......: 2388 
Sample size of model......: 996 
Missing data %............: 58.29146 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    CAD_history + Stroke_history + Peripheral.interv, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age         CAD_history      Stroke_history   Peripheral.interv  
          0.510465           -0.157509           -0.007324           -0.123126            0.197685           -0.159919  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.93275 -0.66240  0.01907  0.64346  2.96767 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.786671   0.630026   1.249  0.21210    
currentDF[, TRAIT]        -0.162944   0.032868  -4.958 8.42e-07 ***
Age                       -0.008232   0.003918  -2.101  0.03587 *  
Gendermale                -0.033556   0.070213  -0.478  0.63282    
Hypertension.compositeyes -0.086195   0.096425  -0.894  0.37159    
DiabetesStatusDiabetes    -0.004985   0.076199  -0.065  0.94785    
SmokerCurrentyes           0.060184   0.069997   0.860  0.39011    
Med.Statin.LLDyes         -0.065859   0.076396  -0.862  0.38886    
Med.all.antiplateletyes   -0.016292   0.103511  -0.157  0.87497    
GFR_MDRD                  -0.002233   0.001666  -1.341  0.18034    
BMI                       -0.007551   0.008672  -0.871  0.38410    
CAD_history               -0.090057   0.071752  -1.255  0.20974    
Stroke_history             0.197606   0.067061   2.947  0.00329 ** 
Peripheral.interv         -0.161279   0.083037  -1.942  0.05240 .  
stenose50-70%              0.023861   0.455875   0.052  0.95827    
stenose70-90%              0.318075   0.440924   0.721  0.47085    
stenose90-99%              0.277050   0.440793   0.629  0.52981    
stenose100% (Occlusion)    0.626538   0.560171   1.118  0.26364    
stenose50-99%              0.198613   0.819108   0.242  0.80846    
stenose70-99%             -0.083839   0.593759  -0.141  0.88774    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9761 on 972 degrees of freedom
Multiple R-squared:  0.05658,   Adjusted R-squared:  0.03814 
F-statistic: 3.068 on 19 and 972 DF,  p-value: 1.101e-05

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.162944 
Standard error............: 0.032868 
Odds ratio (effect size)..: 0.85 
Lower 95% CI..............: 0.797 
Upper 95% CI..............: 0.906 
T-value...................: -4.957502 
P-value...................: 8.422128e-07 
R^2.......................: 0.056578 
Adjusted r^2..............: 0.038137 
Sample size of AE DB......: 2388 
Sample size of model......: 992 
Missing data %............: 58.45896 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + SmokerCurrent + 
    GFR_MDRD + CAD_history + Stroke_history + Peripheral.interv, 
    data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]    SmokerCurrentyes            GFR_MDRD         CAD_history      Stroke_history  
          0.146966           -0.066091            0.105951           -0.002514           -0.129845            0.190585  
 Peripheral.interv  
         -0.173758  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1508 -0.6383  0.0036  0.6501  3.2094 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.665290   0.649313   1.025  0.30582   
currentDF[, TRAIT]        -0.068097   0.033572  -2.028  0.04281 * 
Age                       -0.004320   0.004054  -1.066  0.28681   
Gendermale                 0.003466   0.072569   0.048  0.96192   
Hypertension.compositeyes -0.106848   0.100976  -1.058  0.29026   
DiabetesStatusDiabetes    -0.050711   0.081619  -0.621  0.53454   
SmokerCurrentyes           0.062220   0.073689   0.844  0.39869   
Med.Statin.LLDyes         -0.055150   0.079291  -0.696  0.48690   
Med.all.antiplateletyes   -0.017531   0.109861  -0.160  0.87325   
GFR_MDRD                  -0.003260   0.001759  -1.853  0.06418 . 
BMI                       -0.005782   0.009030  -0.640  0.52214   
CAD_history               -0.094898   0.075609  -1.255  0.20976   
Stroke_history             0.207070   0.070464   2.939  0.00338 **
Peripheral.interv         -0.166293   0.088420  -1.881  0.06033 . 
stenose50-70%             -0.128577   0.465413  -0.276  0.78241   
stenose70-90%              0.179173   0.448315   0.400  0.68950   
stenose90-99%              0.140252   0.447728   0.313  0.75416   
stenose100% (Occlusion)    0.489070   0.569196   0.859  0.39044   
stenose50-99%              0.015460   0.830783   0.019  0.98516   
stenose70-99%             -0.455543   0.667542  -0.682  0.49515   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9909 on 910 degrees of freedom
Multiple R-squared:  0.03919,   Adjusted R-squared:  0.01913 
F-statistic: 1.953 on 19 and 910 DF,  p-value: 0.008611

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.068097 
Standard error............: 0.033572 
Odds ratio (effect size)..: 0.934 
Lower 95% CI..............: 0.875 
Upper 95% CI..............: 0.998 
T-value...................: -2.02839 
P-value...................: 0.04281124 
R^2.......................: 0.039187 
Adjusted r^2..............: 0.019126 
Sample size of AE DB......: 2388 
Sample size of model......: 930 
Missing data %............: 61.05528 

Analysis of IL6R_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD + CAD_history + Peripheral.interv + 
    stenose, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age        Med.Statin.LLDyes                 GFR_MDRD  
               0.254402                 0.138291                -0.008368                -0.371568                -0.003134  
            CAD_history        Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  
              -0.099103                 0.233323                 0.518699                 0.800413                 0.942022  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
               0.655301                 0.565404                -0.108615  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.1926 -0.6496 -0.0011  0.6198  3.0610 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.274225   0.663148   0.414  0.67932    
currentDF[, TRAIT]         0.139450   0.030997   4.499 7.65e-06 ***
Age                       -0.007934   0.003880  -2.045  0.04117 *  
Gendermale                -0.058157   0.068901  -0.844  0.39884    
Hypertension.compositeyes  0.055508   0.095193   0.583  0.55996    
DiabetesStatusDiabetes    -0.077646   0.075649  -1.026  0.30496    
SmokerCurrentyes           0.059294   0.069374   0.855  0.39293    
Med.Statin.LLDyes         -0.373281   0.076205  -4.898 1.13e-06 ***
Med.all.antiplateletyes    0.038778   0.103983   0.373  0.70928    
GFR_MDRD                  -0.003053   0.001682  -1.815  0.06981 .  
BMI                       -0.005349   0.008797  -0.608  0.54328    
CAD_history               -0.074440   0.070919  -1.050  0.29414    
Stroke_history             0.067980   0.066451   1.023  0.30656    
Peripheral.interv          0.233457   0.082462   2.831  0.00473 ** 
stenose50-70%              0.533224   0.503840   1.058  0.29017    
stenose70-90%              0.814412   0.489795   1.663  0.09668 .  
stenose90-99%              0.950282   0.489612   1.941  0.05256 .  
stenose100% (Occlusion)    0.670810   0.598031   1.122  0.26227    
stenose50-99%              0.538437   0.687643   0.783  0.43381    
stenose70-99%             -0.113878   0.629316  -0.181  0.85644    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9703 on 979 degrees of freedom
Multiple R-squared:  0.08311,   Adjusted R-squared:  0.06531 
F-statistic:  4.67 on 19 and 979 DF,  p-value: 1.919e-10

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.13945 
Standard error............: 0.030997 
Odds ratio (effect size)..: 1.15 
Lower 95% CI..............: 1.082 
Upper 95% CI..............: 1.222 
T-value...................: 4.498795 
P-value...................: 7.653324e-06 
R^2.......................: 0.083108 
Adjusted r^2..............: 0.065313 
Sample size of AE DB......: 2388 
Sample size of model......: 999 
Missing data %............: 58.16583 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD + Stroke_history + Peripheral.interv + 
    stenose, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age        Med.Statin.LLDyes                 GFR_MDRD  
               0.190698                 0.062823                -0.008893                -0.373218                -0.002728  
         Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  
               0.096206                 0.210013                 0.503849                 0.820847                 0.949222  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
               0.641657                 0.513585                -0.128979  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3384 -0.6634  0.0112  0.6148  3.0952 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.231609   0.669488   0.346  0.72945    
currentDF[, TRAIT]         0.063184   0.032805   1.926  0.05439 .  
Age                       -0.008096   0.003958  -2.046  0.04107 *  
Gendermale                -0.011671   0.070307  -0.166  0.86819    
Hypertension.compositeyes  0.055446   0.096058   0.577  0.56393    
DiabetesStatusDiabetes    -0.086441   0.076316  -1.133  0.25763    
SmokerCurrentyes           0.041023   0.070053   0.586  0.55828    
Med.Statin.LLDyes         -0.358662   0.077004  -4.658 3.64e-06 ***
Med.all.antiplateletyes    0.027783   0.104869   0.265  0.79111    
GFR_MDRD                  -0.003029   0.001699  -1.782  0.07499 .  
BMI                       -0.005242   0.008888  -0.590  0.55546    
CAD_history               -0.074743   0.071673  -1.043  0.29728    
Stroke_history             0.082865   0.067031   1.236  0.21668    
Peripheral.interv          0.221438   0.083614   2.648  0.00822 ** 
stenose50-70%              0.529972   0.508326   1.043  0.29740    
stenose70-90%              0.840789   0.494103   1.702  0.08914 .  
stenose90-99%              0.964938   0.494012   1.953  0.05107 .  
stenose100% (Occlusion)    0.633483   0.603460   1.050  0.29409    
stenose50-99%              0.515776   0.693804   0.743  0.45742    
stenose70-99%             -0.098253   0.634854  -0.155  0.87704    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9785 on 975 degrees of freedom
Multiple R-squared:  0.06621,   Adjusted R-squared:  0.04801 
F-statistic: 3.638 on 19 and 975 DF,  p-value: 2.505e-07

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: 0.063184 
Standard error............: 0.032805 
Odds ratio (effect size)..: 1.065 
Lower 95% CI..............: 0.999 
Upper 95% CI..............: 1.136 
T-value...................: 1.926004 
P-value...................: 0.05439434 
R^2.......................: 0.066208 
Adjusted r^2..............: 0.048011 
Sample size of AE DB......: 2388 
Sample size of model......: 995 
Missing data %............: 58.33333 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    DiabetesStatus + Med.Statin.LLD + GFR_MDRD + CAD_history + 
    Peripheral.interv + stenose, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age   DiabetesStatusDiabetes        Med.Statin.LLDyes  
               0.320815                 0.075290                -0.010262                -0.132040                -0.344402  
               GFR_MDRD              CAD_history        Peripheral.interv            stenose50-70%            stenose70-90%  
              -0.003693                -0.111030                 0.215654                 0.616697                 0.932206  
          stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
               1.048426                 0.702021                 0.580092                 0.024660  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3853 -0.6424 -0.0008  0.5946  3.0704 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.373101   0.681739   0.547  0.58432    
currentDF[, TRAIT]         0.072980   0.033447   2.182  0.02937 *  
Age                       -0.010508   0.004055  -2.591  0.00971 ** 
Gendermale                -0.045887   0.071954  -0.638  0.52381    
Hypertension.compositeyes  0.058845   0.099784   0.590  0.55552    
DiabetesStatusDiabetes    -0.127065   0.080829  -1.572  0.11629    
SmokerCurrentyes           0.028334   0.072970   0.388  0.69789    
Med.Statin.LLDyes         -0.349043   0.079153  -4.410 1.16e-05 ***
Med.all.antiplateletyes   -0.007124   0.110152  -0.065  0.94844    
GFR_MDRD                  -0.003437   0.001780  -1.931  0.05382 .  
BMI                       -0.003941   0.009220  -0.427  0.66914    
CAD_history               -0.096726   0.074536  -1.298  0.19472    
Stroke_history             0.080324   0.069644   1.153  0.24907    
Peripheral.interv          0.214445   0.087779   2.443  0.01475 *  
stenose50-70%              0.614862   0.512332   1.200  0.23040    
stenose70-90%              0.932201   0.496101   1.879  0.06056 .  
stenose90-99%              1.047922   0.495616   2.114  0.03475 *  
stenose100% (Occlusion)    0.712439   0.605740   1.176  0.23984    
stenose50-99%              0.549565   0.695939   0.790  0.42992    
stenose70-99%              0.027142   0.697472   0.039  0.96897    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9817 on 912 degrees of freedom
Multiple R-squared:  0.06911,   Adjusted R-squared:  0.04972 
F-statistic: 3.564 on 19 and 912 DF,  p-value: 4.323e-07

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: 0.07298 
Standard error............: 0.033447 
Odds ratio (effect size)..: 1.076 
Lower 95% CI..............: 1.007 
Upper 95% CI..............: 1.149 
T-value...................: 2.181987 
P-value...................: 0.02936508 
R^2.......................: 0.069115 
Adjusted r^2..............: 0.049721 
Sample size of AE DB......: 2388 
Sample size of model......: 932 
Missing data %............: 60.97152 

Analysis of MCP1_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    Med.Statin.LLD + Stroke_history, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes          Med.Statin.LLDyes             Stroke_history  
                  0.26759                   -0.05375                   -0.20588                   -0.15187                    0.10863  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3648 -0.6660 -0.0273  0.6576  3.2393 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                0.2124414  0.6391679   0.332   0.7397  
currentDF[, TRAIT]        -0.0594184  0.0313284  -1.897   0.0582 .
Age                       -0.0020052  0.0038961  -0.515   0.6069  
Gendermale                 0.0857202  0.0691441   1.240   0.2154  
Hypertension.compositeyes -0.1946501  0.0966828  -2.013   0.0443 *
DiabetesStatusDiabetes    -0.0356636  0.0763648  -0.467   0.6406  
SmokerCurrentyes          -0.0229331  0.0699413  -0.328   0.7431  
Med.Statin.LLDyes         -0.1530759  0.0772298  -1.982   0.0477 *
Med.all.antiplateletyes   -0.0060653  0.1048021  -0.058   0.9539  
GFR_MDRD                  -0.0007891  0.0016718  -0.472   0.6370  
BMI                       -0.0033761  0.0086790  -0.389   0.6974  
CAD_history               -0.0429034  0.0715219  -0.600   0.5487  
Stroke_history             0.1049280  0.0673948   1.557   0.1198  
Peripheral.interv          0.0201952  0.0827808   0.244   0.8073  
stenose50-70%              0.2746486  0.4665000   0.589   0.5562  
stenose70-90%              0.3718106  0.4510306   0.824   0.4099  
stenose90-99%              0.2388238  0.4508856   0.530   0.5965  
stenose100% (Occlusion)   -0.1876128  0.5732084  -0.327   0.7435  
stenose50-99%              0.5643377  0.6719949   0.840   0.4012  
stenose70-99%              0.5005227  0.6075952   0.824   0.4103  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9995 on 1017 degrees of freedom
Multiple R-squared:  0.02434,   Adjusted R-squared:  0.006109 
F-statistic: 1.335 on 19 and 1017 DF,  p-value: 0.152

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: -0.059418 
Standard error............: 0.031328 
Odds ratio (effect size)..: 0.942 
Lower 95% CI..............: 0.886 
Upper 95% CI..............: 1.002 
T-value...................: -1.896633 
P-value...................: 0.05815957 
R^2.......................: 0.024337 
Adjusted r^2..............: 0.006109 
Sample size of AE DB......: 2388 
Sample size of model......: 1037 
Missing data %............: 56.57454 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    Med.Statin.LLD + Stroke_history, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes          Med.Statin.LLDyes             Stroke_history  
                  0.27925                   -0.10750                   -0.20781                   -0.15930                    0.09907  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3724 -0.6767 -0.0304  0.6444  3.2770 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.290952   0.638800   0.455  0.64887   
currentDF[, TRAIT]        -0.107077   0.032598  -3.285  0.00106 **
Age                       -0.003437   0.003929  -0.875  0.38199   
Gendermale                 0.040662   0.069945   0.581  0.56115   
Hypertension.compositeyes -0.190633   0.096558  -1.974  0.04862 * 
DiabetesStatusDiabetes    -0.029269   0.076250  -0.384  0.70116   
SmokerCurrentyes          -0.017656   0.069895  -0.253  0.80062   
Med.Statin.LLDyes         -0.166237   0.077217  -2.153  0.03157 * 
Med.all.antiplateletyes   -0.011125   0.104641  -0.106  0.91535   
GFR_MDRD                  -0.000614   0.001672  -0.367  0.71354   
BMI                       -0.003254   0.008677  -0.375  0.70773   
CAD_history               -0.036723   0.071522  -0.513  0.60775   
Stroke_history             0.097831   0.067275   1.454  0.14620   
Peripheral.interv          0.024988   0.083042   0.301  0.76354   
stenose50-70%              0.320528   0.465811   0.688  0.49154   
stenose70-90%              0.410560   0.450412   0.912  0.36224   
stenose90-99%              0.287171   0.450366   0.638  0.52385   
stenose100% (Occlusion)   -0.118251   0.572338  -0.207  0.83636   
stenose50-99%              0.632607   0.670986   0.943  0.34601   
stenose70-99%              0.549146   0.606689   0.905  0.36560   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9976 on 1013 degrees of freedom
Multiple R-squared:  0.031, Adjusted R-squared:  0.01282 
F-statistic: 1.705 on 19 and 1013 DF,  p-value: 0.02996

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.107077 
Standard error............: 0.032598 
Odds ratio (effect size)..: 0.898 
Lower 95% CI..............: 0.843 
Upper 95% CI..............: 0.958 
T-value...................: -3.284785 
P-value...................: 0.001055572 
R^2.......................: 0.030996 
Adjusted r^2..............: 0.012821 
Sample size of AE DB......: 2388 
Sample size of model......: 1033 
Missing data %............: 56.74204 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    Med.Statin.LLD + Stroke_history, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes          Med.Statin.LLDyes             Stroke_history  
                   0.2520                    -0.1458                    -0.1882                    -0.1728                     0.1253  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2433 -0.6661 -0.0246  0.6386  3.3145 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.211623   0.650353   0.325   0.7450    
currentDF[, TRAIT]        -0.142888   0.033041  -4.325 1.69e-05 ***
Age                       -0.001066   0.004023  -0.265   0.7910    
Gendermale                 0.102130   0.071463   1.429   0.1533    
Hypertension.compositeyes -0.184342   0.100225  -1.839   0.0662 .  
DiabetesStatusDiabetes    -0.041305   0.080612  -0.512   0.6085    
SmokerCurrentyes          -0.012813   0.072721  -0.176   0.8602    
Med.Statin.LLDyes         -0.172532   0.079341  -2.175   0.0299 *  
Med.all.antiplateletyes    0.032519   0.109658   0.297   0.7669    
GFR_MDRD                  -0.001284   0.001744  -0.736   0.4619    
BMI                       -0.002257   0.008979  -0.251   0.8016    
CAD_history               -0.038172   0.074308  -0.514   0.6076    
Stroke_history             0.119804   0.069831   1.716   0.0866 .  
Peripheral.interv          0.002460   0.087239   0.028   0.9775    
stenose50-70%              0.112862   0.469976   0.240   0.8103    
stenose70-90%              0.252067   0.452625   0.557   0.5777    
stenose90-99%              0.135540   0.452129   0.300   0.7644    
stenose100% (Occlusion)   -0.268393   0.574820  -0.467   0.6407    
stenose50-99%              0.600081   0.673103   0.892   0.3729    
stenose70-99%              0.162929   0.674146   0.242   0.8091    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.001 on 949 degrees of freedom
Multiple R-squared:  0.0426,    Adjusted R-squared:  0.02343 
F-statistic: 2.222 on 19 and 949 DF,  p-value: 0.001956

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.142888 
Standard error............: 0.033041 
Odds ratio (effect size)..: 0.867 
Lower 95% CI..............: 0.812 
Upper 95% CI..............: 0.925 
T-value...................: -4.324596 
P-value...................: 1.689717e-05 
R^2.......................: 0.0426 
Adjusted r^2..............: 0.023432 
Sample size of AE DB......: 2388 
Sample size of model......: 969 
Missing data %............: 59.42211 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    GFR_MDRD + Stroke_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]              GFR_MDRD        Stroke_history  
             1.13102               0.16676              -0.01009              -0.39756  

Degrees of Freedom: 480 Total (i.e. Null);  477 Residual
Null Deviance:      656.9 
Residual Deviance: 646.2    AIC: 654.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8778  -1.2477   0.8434   1.0452   1.6363  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -1.142674   1.894433  -0.603   0.5464  
currentDF[, PROTEIN]       0.187641   0.099762   1.881   0.0600 .
Age                        0.005528   0.012359   0.447   0.6547  
Gendermale                -0.115162   0.218320  -0.527   0.5979  
Hypertension.compositeyes  0.476111   0.285756   1.666   0.0957 .
DiabetesStatusDiabetes    -0.321528   0.244112  -1.317   0.1878  
SmokerCurrentyes           0.107466   0.208795   0.515   0.6068  
Med.Statin.LLDyes          0.116419   0.219777   0.530   0.5963  
Med.all.antiplateletyes    0.394578   0.343182   1.150   0.2502  
GFR_MDRD                  -0.010480   0.005580  -1.878   0.0603 .
BMI                       -0.004079   0.026551  -0.154   0.8779  
CAD_history               -0.050297   0.216490  -0.232   0.8163  
Stroke_history            -0.449958   0.205657  -2.188   0.0287 *
Peripheral.interv         -0.283628   0.243087  -1.167   0.2433  
stenose50-70%              1.612811   1.359371   1.186   0.2354  
stenose70-90%              1.574761   1.264171   1.246   0.2129  
stenose90-99%              1.246104   1.258956   0.990   0.3223  
stenose100% (Occlusion)    1.636060   1.495055   1.094   0.2738  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 656.88  on 480  degrees of freedom
Residual deviance: 635.08  on 463  degrees of freedom
AIC: 671.08

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.187641 
Standard error............: 0.099762 
Odds ratio (effect size)..: 1.206 
Lower 95% CI..............: 0.992 
Upper 95% CI..............: 1.467 
Z-value...................: 1.880896 
P-value...................: 0.05998602 
Hosmer and Lemeshow r^2...: 0.033177 
Cox and Snell r^2.........: 0.044296 
Nagelkerke's pseudo r^2...: 0.059475 
Sample size of AE DB......: 2388 
Sample size of model......: 481 
Missing data %............: 79.85762 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent + 
    Peripheral.interv + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)         SmokerCurrentyes        Peripheral.interv            stenose50-70%            stenose70-90%  
                16.6172                   0.5118                  -0.5187                  -0.1656                 -15.0805  
          stenose90-99%  stenose100% (Occlusion)  
               -15.4792                 -14.7976  

Degrees of Freedom: 479 Total (i.e. Null);  473 Residual
Null Deviance:      474.8 
Residual Deviance: 455.6    AIC: 469.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1410   0.4147   0.5880   0.7112   1.1491  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.833e+01  2.202e+03   0.008   0.9934  
currentDF[, PROTEIN]      -1.131e-01  1.218e-01  -0.928   0.3532  
Age                       -5.156e-03  1.583e-02  -0.326   0.7447  
Gendermale                -1.905e-01  2.800e-01  -0.680   0.4962  
Hypertension.compositeyes  3.476e-01  3.454e-01   1.006   0.3142  
DiabetesStatusDiabetes     1.851e-01  3.131e-01   0.591   0.5545  
SmokerCurrentyes           5.231e-01  2.725e-01   1.920   0.0549 .
Med.Statin.LLDyes         -1.921e-02  2.739e-01  -0.070   0.9441  
Med.all.antiplateletyes    5.197e-01  3.990e-01   1.303   0.1927  
GFR_MDRD                  -4.836e-03  7.078e-03  -0.683   0.4945  
BMI                       -2.662e-02  3.364e-02  -0.791   0.4287  
CAD_history                9.202e-02  2.697e-01   0.341   0.7330  
Stroke_history             1.321e-01  2.634e-01   0.502   0.6159  
Peripheral.interv         -5.602e-01  2.846e-01  -1.969   0.0490 *
stenose50-70%             -1.090e-01  2.395e+03   0.000   1.0000  
stenose70-90%             -1.607e+01  2.202e+03  -0.007   0.9942  
stenose90-99%             -1.653e+01  2.202e+03  -0.008   0.9940  
stenose100% (Occlusion)   -1.556e+01  2.202e+03  -0.007   0.9944  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 474.79  on 479  degrees of freedom
Residual deviance: 449.41  on 462  degrees of freedom
AIC: 485.41

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.113087 
Standard error............: 0.121798 
Odds ratio (effect size)..: 0.893 
Lower 95% CI..............: 0.703 
Upper 95% CI..............: 1.134 
Z-value...................: -0.928484 
P-value...................: 0.3531568 
Hosmer and Lemeshow r^2...: 0.053459 
Cox and Snell r^2.........: 0.051505 
Nagelkerke's pseudo r^2...: 0.082 
Sample size of AE DB......: 2388 
Sample size of model......: 480 
Missing data %............: 79.8995 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite + 
    DiabetesStatus + Stroke_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)                 Gendermale  Hypertension.compositeyes     DiabetesStatusDiabetes             Stroke_history  
                   0.2536                     0.7695                     0.6023                    -0.5270                     0.7668  

Degrees of Freedom: 480 Total (i.e. Null);  476 Residual
Null Deviance:      483.6 
Residual Deviance: 459.4    AIC: 469.4

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4561   0.3832   0.5522   0.7048   1.3298  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                13.557573 833.530464   0.016 0.987023    
currentDF[, PROTEIN]        0.105467   0.121292   0.870 0.384555    
Age                        -0.008356   0.015529  -0.538 0.590504    
Gendermale                  0.854341   0.255518   3.344 0.000827 ***
Hypertension.compositeyes   0.653898   0.331496   1.973 0.048545 *  
DiabetesStatusDiabetes     -0.535682   0.288011  -1.860 0.062894 .  
SmokerCurrentyes            0.235127   0.266099   0.884 0.376909    
Med.Statin.LLDyes          -0.270417   0.289334  -0.935 0.349985    
Med.all.antiplateletyes     0.332141   0.405082   0.820 0.412253    
GFR_MDRD                   -0.004346   0.007159  -0.607 0.543790    
BMI                         0.025693   0.033034   0.778 0.436702    
CAD_history                -0.100692   0.271063  -0.371 0.710285    
Stroke_history              0.725302   0.285075   2.544 0.010951 *  
Peripheral.interv          -0.253404   0.288452  -0.878 0.379674    
stenose50-70%             -14.083308 833.528719  -0.017 0.986520    
stenose70-90%             -13.205044 833.528557  -0.016 0.987360    
stenose90-99%             -13.322823 833.528546  -0.016 0.987247    
stenose100% (Occlusion)   -12.920301 833.529308  -0.016 0.987633    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 483.60  on 480  degrees of freedom
Residual deviance: 450.33  on 463  degrees of freedom
AIC: 486.33

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.105467 
Standard error............: 0.121292 
Odds ratio (effect size)..: 1.111 
Lower 95% CI..............: 0.876 
Upper 95% CI..............: 1.409 
Z-value...................: 0.869535 
P-value...................: 0.3845546 
Hosmer and Lemeshow r^2...: 0.068787 
Cox and Snell r^2.........: 0.066821 
Nagelkerke's pseudo r^2...: 0.105379 
Sample size of AE DB......: 2388 
Sample size of model......: 481 
Missing data %............: 79.85762 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    DiabetesStatus + BMI + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
           (Intercept)                     Age              Gendermale  DiabetesStatusDiabetes                     BMI  
              -2.71906                 0.03306                 0.64772                -0.58799                 0.04663  
     Peripheral.interv  
               0.47594  

Degrees of Freedom: 480 Total (i.e. Null);  475 Residual
Null Deviance:      538.2 
Residual Deviance: 514.6    AIC: 526.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1197   0.3817   0.6180   0.7868   1.3147  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)   
(Intercept)               -3.724347   2.077712  -1.793  0.07305 . 
currentDF[, PROTEIN]       0.031883   0.112786   0.283  0.77742   
Age                        0.029801   0.014081   2.116  0.03431 * 
Gendermale                 0.677626   0.239517   2.829  0.00467 **
Hypertension.compositeyes  0.362423   0.313321   1.157  0.24739   
DiabetesStatusDiabetes    -0.638623   0.268045  -2.383  0.01719 * 
SmokerCurrentyes           0.133395   0.241854   0.552  0.58125   
Med.Statin.LLDyes          0.051499   0.255894   0.201  0.84050   
Med.all.antiplateletyes   -0.278418   0.430917  -0.646  0.51821   
GFR_MDRD                  -0.004065   0.006460  -0.629  0.52923   
BMI                        0.046769   0.030726   1.522  0.12797   
CAD_history                0.176307   0.258493   0.682  0.49520   
Stroke_history             0.049418   0.239972   0.206  0.83685   
Peripheral.interv          0.442713   0.300800   1.472  0.14108   
stenose50-70%              1.305170   1.391394   0.938  0.34823   
stenose70-90%              1.209027   1.271245   0.951  0.34158   
stenose90-99%              1.359738   1.267498   1.073  0.28337   
stenose100% (Occlusion)    1.878451   1.699793   1.105  0.26911   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 538.20  on 480  degrees of freedom
Residual deviance: 509.11  on 463  degrees of freedom
AIC: 545.11

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.031883 
Standard error............: 0.112786 
Odds ratio (effect size)..: 1.032 
Lower 95% CI..............: 0.828 
Upper 95% CI..............: 1.288 
Z-value...................: 0.282686 
P-value...................: 0.7774176 
Hosmer and Lemeshow r^2...: 0.054057 
Cox and Snell r^2.........: 0.058693 
Nagelkerke's pseudo r^2...: 0.087163 
Sample size of AE DB......: 2388 
Sample size of model......: 481 
Missing data %............: 79.85762 

Analysis of MCP1_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    DiabetesStatus + GFR_MDRD + Stroke_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
           (Intercept)    currentDF[, PROTEIN]  DiabetesStatusDiabetes                GFR_MDRD          Stroke_history  
              1.192770               -0.151103               -0.450022               -0.009032               -0.335659  

Degrees of Freedom: 513 Total (i.e. Null);  509 Residual
Null Deviance:      698.1 
Residual Deviance: 685.4    AIC: 695.4

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6902  -1.2563   0.8474   1.0134   1.4941  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -1.341239   1.847433  -0.726   0.4678  
currentDF[, PROTEIN]      -0.143925   0.094902  -1.517   0.1294  
Age                        0.008539   0.012074   0.707   0.4794  
Gendermale                -0.133609   0.211820  -0.631   0.5282  
Hypertension.compositeyes  0.421229   0.275933   1.527   0.1269  
DiabetesStatusDiabetes    -0.491397   0.233262  -2.107   0.0351 *
SmokerCurrentyes           0.190422   0.202282   0.941   0.3465  
Med.Statin.LLDyes         -0.065611   0.216144  -0.304   0.7615  
Med.all.antiplateletyes    0.294148   0.328102   0.897   0.3700  
GFR_MDRD                  -0.008637   0.005278  -1.636   0.1018  
BMI                        0.006020   0.025217   0.239   0.8113  
CAD_history               -0.019955   0.210077  -0.095   0.9243  
Stroke_history            -0.382267   0.196922  -1.941   0.0522 .
Peripheral.interv         -0.296732   0.245678  -1.208   0.2271  
stenose50-70%              1.345003   1.366009   0.985   0.3248  
stenose70-90%              1.474735   1.278492   1.153   0.2487  
stenose90-99%              1.183503   1.274224   0.929   0.3530  
stenose100% (Occlusion)    1.433390   1.603296   0.894   0.3713  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 698.10  on 513  degrees of freedom
Residual deviance: 676.62  on 496  degrees of freedom
AIC: 712.62

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.143925 
Standard error............: 0.094902 
Odds ratio (effect size)..: 0.866 
Lower 95% CI..............: 0.719 
Upper 95% CI..............: 1.043 
Z-value...................: -1.516566 
P-value...................: 0.1293764 
Hosmer and Lemeshow r^2...: 0.030769 
Cox and Snell r^2.........: 0.040928 
Nagelkerke's pseudo r^2...: 0.055094 
Sample size of AE DB......: 2388 
Sample size of model......: 514 
Missing data %............: 78.47571 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerCurrent + Med.all.antiplatelet + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]         SmokerCurrentyes  Med.all.antiplateletyes        Peripheral.interv  
                15.0605                  -0.6325                   0.5715                   0.8102                  -0.5723  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
               -13.0595                 -14.0415                 -14.5745                 -14.0760  

Degrees of Freedom: 511 Total (i.e. Null);  503 Residual
Null Deviance:      505.7 
Residual Deviance: 459.5    AIC: 477.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5465   0.3165   0.5104   0.6799   1.4542  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                15.999553 778.354658   0.021   0.9836    
currentDF[, PROTEIN]       -0.622928   0.128740  -4.839 1.31e-06 ***
Age                        -0.005270   0.015877  -0.332   0.7399    
Gendermale                 -0.109271   0.281106  -0.389   0.6975    
Hypertension.compositeyes   0.232635   0.346594   0.671   0.5021    
DiabetesStatusDiabetes      0.322371   0.320516   1.006   0.3145    
SmokerCurrentyes            0.569775   0.272057   2.094   0.0362 *  
Med.Statin.LLDyes           0.069345   0.268917   0.258   0.7965    
Med.all.antiplateletyes     0.802899   0.388040   2.069   0.0385 *  
GFR_MDRD                   -0.002382   0.006955  -0.343   0.7320    
BMI                        -0.025431   0.033840  -0.752   0.4523    
CAD_history                 0.128771   0.267709   0.481   0.6305    
Stroke_history              0.133460   0.258321   0.517   0.6054    
Peripheral.interv          -0.629923   0.299489  -2.103   0.0354 *  
stenose50-70%             -13.094548 778.353270  -0.017   0.9866    
stenose70-90%             -14.077384 778.352576  -0.018   0.9856    
stenose90-99%             -14.620319 778.352564  -0.019   0.9850    
stenose100% (Occlusion)   -14.008097 778.353486  -0.018   0.9856    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 505.69  on 511  degrees of freedom
Residual deviance: 456.43  on 494  degrees of freedom
AIC: 492.43

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.622928 
Standard error............: 0.12874 
Odds ratio (effect size)..: 0.536 
Lower 95% CI..............: 0.417 
Upper 95% CI..............: 0.69 
Z-value...................: -4.83865 
P-value...................: 1.307237e-06 
Hosmer and Lemeshow r^2...: 0.097413 
Cox and Snell r^2.........: 0.091729 
Nagelkerke's pseudo r^2...: 0.146168 
Sample size of AE DB......: 2388 
Sample size of model......: 512 
Missing data %............: 78.55946 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Hypertension.composite + Stroke_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]                 Gendermale  Hypertension.compositeyes             Stroke_history  
                   0.4409                     0.6533                     0.5583                     0.6017                     0.6751  

Degrees of Freedom: 513 Total (i.e. Null);  509 Residual
Null Deviance:      506.6 
Residual Deviance: 459.5    AIC: 469.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5740   0.2922   0.4930   0.6720   1.5683  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                12.661734 817.641489   0.015   0.9876    
currentDF[, PROTEIN]        0.656649   0.132397   4.960 7.06e-07 ***
Age                         0.001732   0.015546   0.111   0.9113    
Gendermale                  0.612693   0.256582   2.388   0.0169 *  
Hypertension.compositeyes   0.685985   0.341187   2.011   0.0444 *  
DiabetesStatusDiabetes     -0.262261   0.294365  -0.891   0.3730    
SmokerCurrentyes            0.363236   0.268874   1.351   0.1767    
Med.Statin.LLDyes          -0.147175   0.293177  -0.502   0.6157    
Med.all.antiplateletyes     0.038168   0.411206   0.093   0.9260    
GFR_MDRD                   -0.001070   0.007032  -0.152   0.8790    
BMI                         0.041989   0.032506   1.292   0.1965    
CAD_history                -0.184485   0.275017  -0.671   0.5023    
Stroke_history              0.635540   0.279528   2.274   0.0230 *  
Peripheral.interv          -0.174872   0.301205  -0.581   0.5615    
stenose50-70%             -14.255551 817.639782  -0.017   0.9861    
stenose70-90%             -13.148729 817.639612  -0.016   0.9872    
stenose90-99%             -13.458669 817.639598  -0.016   0.9869    
stenose100% (Occlusion)   -13.167887 817.640432  -0.016   0.9872    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 506.55  on 513  degrees of freedom
Residual deviance: 448.02  on 496  degrees of freedom
AIC: 484.02

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.656649 
Standard error............: 0.132397 
Odds ratio (effect size)..: 1.928 
Lower 95% CI..............: 1.488 
Upper 95% CI..............: 2.5 
Z-value...................: 4.959683 
P-value...................: 7.060839e-07 
Hosmer and Lemeshow r^2...: 0.115562 
Cox and Snell r^2.........: 0.107642 
Nagelkerke's pseudo r^2...: 0.171745 
Sample size of AE DB......: 2388 
Sample size of model......: 514 
Missing data %............: 78.47571 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    DiabetesStatus + BMI + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
           (Intercept)                     Age              Gendermale  DiabetesStatusDiabetes                     BMI  
              -2.41015                 0.02549                 0.72976                -0.55880                 0.05326  
     Peripheral.interv  
               0.46653  

Degrees of Freedom: 513 Total (i.e. Null);  508 Residual
Null Deviance:      568 
Residual Deviance: 544  AIC: 556

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1812   0.4279   0.6129   0.7610   1.5453  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)   
(Intercept)               -3.337489   2.007482  -1.663  0.09641 . 
currentDF[, PROTEIN]       0.114993   0.110404   1.042  0.29761   
Age                        0.021692   0.013848   1.566  0.11724   
Gendermale                 0.699221   0.231008   3.027  0.00247 **
Hypertension.compositeyes  0.332762   0.307165   1.083  0.27866   
DiabetesStatusDiabetes    -0.570391   0.259801  -2.195  0.02813 * 
SmokerCurrentyes           0.111871   0.236363   0.473  0.63600   
Med.Statin.LLDyes         -0.055247   0.255070  -0.217  0.82852   
Med.all.antiplateletyes   -0.189324   0.397135  -0.477  0.63356   
GFR_MDRD                  -0.003295   0.006174  -0.534  0.59352   
BMI                        0.051262   0.029169   1.757  0.07884 . 
CAD_history                0.218815   0.255681   0.856  0.39210   
Stroke_history             0.130905   0.232489   0.563  0.57339   
Peripheral.interv          0.448115   0.306829   1.460  0.14416   
stenose50-70%              1.269161   1.380367   0.919  0.35787   
stenose70-90%              1.185950   1.267384   0.936  0.34940   
stenose90-99%              1.355527   1.264520   1.072  0.28373   
stenose100% (Occlusion)    1.465751   1.720671   0.852  0.39430   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 567.98  on 513  degrees of freedom
Residual deviance: 538.07  on 496  degrees of freedom
AIC: 574.07

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.114993 
Standard error............: 0.110404 
Odds ratio (effect size)..: 1.122 
Lower 95% CI..............: 0.904 
Upper 95% CI..............: 1.393 
Z-value...................: 1.041567 
P-value...................: 0.2976126 
Hosmer and Lemeshow r^2...: 0.052662 
Cox and Snell r^2.........: 0.056532 
Nagelkerke's pseudo r^2...: 0.084528 
Sample size of AE DB......: 2388 
Sample size of model......: 514 
Missing data %............: 78.47571 

Analysis of IL6_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age         SmokerCurrentyes              CAD_history  
               -1.21906                 -0.10139                  0.01999                  0.39841                  0.25581  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%  
               -1.00567                 -0.50697                 -0.26246                  0.80569                -13.93538  
          stenose70-99%  
               -1.53003  

Degrees of Freedom: 996 Total (i.e. Null);  986 Residual
Null Deviance:      1381 
Residual Deviance: 1349     AIC: 1371

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6147  -1.1333  -0.7964   1.1564   1.6722  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                -0.909944   1.321284  -0.689  0.49102   
currentDF[, PROTEIN]       -0.099976   0.066163  -1.511  0.13077   
Age                         0.017028   0.008100   2.102  0.03552 * 
Gendermale                 -0.132850   0.144146  -0.922  0.35672   
Hypertension.compositeyes   0.232383   0.202519   1.147  0.25119   
DiabetesStatusDiabetes     -0.175024   0.159524  -1.097  0.27257   
SmokerCurrentyes            0.413549   0.146703   2.819  0.00482 **
Med.Statin.LLDyes          -0.164639   0.159153  -1.034  0.30092   
Med.all.antiplateletyes    -0.223519   0.217193  -1.029  0.30342   
GFR_MDRD                   -0.001873   0.003492  -0.536  0.59169   
BMI                         0.012482   0.018095   0.690  0.49034   
CAD_history                 0.264314   0.149945   1.763  0.07795 . 
Stroke_history             -0.138429   0.140758  -0.983  0.32538   
Peripheral.interv          -0.183619   0.172919  -1.062  0.28829   
stenose50-70%              -0.939975   0.962505  -0.977  0.32877   
stenose70-90%              -0.485844   0.928836  -0.523  0.60093   
stenose90-99%              -0.236697   0.928355  -0.255  0.79875   
stenose100% (Occlusion)     0.801826   1.244197   0.644  0.51928   
stenose50-99%             -14.007873 368.424975  -0.038  0.96967   
stenose70-99%              -1.453878   1.253623  -1.160  0.24615   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1381.3  on 996  degrees of freedom
Residual deviance: 1340.3  on 977  degrees of freedom
AIC: 1380.3

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.099976 
Standard error............: 0.066163 
Odds ratio (effect size)..: 0.905 
Lower 95% CI..............: 0.795 
Upper 95% CI..............: 1.03 
Z-value...................: -1.511065 
P-value...................: 0.1307718 
Hosmer and Lemeshow r^2...: 0.029706 
Cox and Snell r^2.........: 0.04032 
Nagelkerke's pseudo r^2...: 0.053776 
Sample size of AE DB......: 2388 
Sample size of model......: 997 
Missing data %............: 58.24958 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Hypertension.composite + SmokerCurrent + BMI + Stroke_history, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]  Hypertension.compositeyes           SmokerCurrentyes                        BMI  
                 -0.01981                   -0.29027                    0.34009                    0.44475                    0.03349  
           Stroke_history  
                  0.25226  

Degrees of Freedom: 999 Total (i.e. Null);  994 Residual
Null Deviance:      1017 
Residual Deviance: 993  AIC: 1005

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2838   0.4520   0.6083   0.7205   1.0945  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.292e+01  6.481e+02   0.020 0.984089    
currentDF[, PROTEIN]      -2.788e-01  8.257e-02  -3.376 0.000735 ***
Age                        8.333e-03  9.876e-03   0.844 0.398752    
Gendermale                -8.397e-02  1.781e-01  -0.471 0.637300    
Hypertension.compositeyes  3.107e-01  2.330e-01   1.334 0.182365    
DiabetesStatusDiabetes     2.105e-01  2.039e-01   1.033 0.301764    
SmokerCurrentyes           4.917e-01  1.863e-01   2.639 0.008305 ** 
Med.Statin.LLDyes         -1.661e-02  1.955e-01  -0.085 0.932280    
Med.all.antiplateletyes    1.316e-01  2.617e-01   0.503 0.615014    
GFR_MDRD                   4.714e-03  4.302e-03   1.096 0.273176    
BMI                        3.356e-02  2.363e-02   1.420 0.155532    
CAD_history                2.203e-01  1.882e-01   1.171 0.241679    
Stroke_history             2.405e-01  1.765e-01   1.363 0.172966    
Peripheral.interv         -1.820e-02  2.155e-01  -0.084 0.932671    
stenose50-70%             -1.361e+01  6.481e+02  -0.021 0.983249    
stenose70-90%             -1.401e+01  6.481e+02  -0.022 0.982747    
stenose90-99%             -1.406e+01  6.481e+02  -0.022 0.982690    
stenose100% (Occlusion)    4.945e-01  8.183e+02   0.001 0.999518    
stenose50-99%              1.313e-02  1.208e+03   0.000 0.999991    
stenose70-99%             -1.373e+01  6.481e+02  -0.021 0.983103    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1017.22  on 999  degrees of freedom
Residual deviance:  980.18  on 980  degrees of freedom
AIC: 1020.2

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.278762 
Standard error............: 0.08257 
Odds ratio (effect size)..: 0.757 
Lower 95% CI..............: 0.644 
Upper 95% CI..............: 0.89 
Z-value...................: -3.376059 
P-value...................: 0.0007353204 
Hosmer and Lemeshow r^2...: 0.036411 
Cox and Snell r^2.........: 0.036361 
Nagelkerke's pseudo r^2...: 0.056956 
Sample size of AE DB......: 2388 
Sample size of model......: 1000 
Missing data %............: 58.12395 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale        Stroke_history     Peripheral.interv  
              0.5014                0.4505                0.8327                0.3722               -0.6087  

Degrees of Freedom: 999 Total (i.e. Null);  995 Residual
Null Deviance:      1165 
Residual Deviance: 1079     AIC: 1089

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2290  -1.0455   0.6047   0.8064   2.0491  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.317e+01  3.905e+02   0.034 0.973093    
currentDF[, PROTEIN]       4.532e-01  7.890e-02   5.744 9.26e-09 ***
Age                        9.621e-03  9.328e-03   1.031 0.302330    
Gendermale                 8.512e-01  1.611e-01   5.282 1.27e-07 ***
Hypertension.compositeyes  6.829e-02  2.349e-01   0.291 0.771250    
DiabetesStatusDiabetes    -1.155e-01  1.839e-01  -0.628 0.529991    
SmokerCurrentyes           1.014e-01  1.710e-01   0.593 0.553366    
Med.Statin.LLDyes         -1.768e-01  1.904e-01  -0.928 0.353200    
Med.all.antiplateletyes    6.371e-02  2.501e-01   0.255 0.798916    
GFR_MDRD                  -2.636e-04  4.098e-03  -0.064 0.948718    
BMI                        3.776e-03  2.044e-02   0.185 0.853460    
CAD_history                7.889e-02  1.751e-01   0.451 0.652297    
Stroke_history             3.768e-01  1.703e-01   2.213 0.026912 *  
Peripheral.interv         -6.136e-01  1.860e-01  -3.299 0.000972 ***
stenose50-70%             -1.353e+01  3.905e+02  -0.035 0.972366    
stenose70-90%             -1.348e+01  3.905e+02  -0.035 0.972456    
stenose90-99%             -1.333e+01  3.905e+02  -0.034 0.972772    
stenose100% (Occlusion)   -1.427e+01  3.905e+02  -0.037 0.970836    
stenose50-99%             -1.498e+01  3.905e+02  -0.038 0.969387    
stenose70-99%             -1.461e+01  3.905e+02  -0.037 0.970150    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1164.5  on 999  degrees of freedom
Residual deviance: 1067.6  on 980  degrees of freedom
AIC: 1107.6

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.453209 
Standard error............: 0.078905 
Odds ratio (effect size)..: 1.573 
Lower 95% CI..............: 1.348 
Upper 95% CI..............: 1.837 
Z-value...................: 5.743761 
P-value...................: 9.259659e-09 
Hosmer and Lemeshow r^2...: 0.083261 
Cox and Snell r^2.........: 0.092407 
Nagelkerke's pseudo r^2...: 0.134327 
Sample size of AE DB......: 2388 
Sample size of model......: 1000 
Missing data %............: 58.12395 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Med.Statin.LLD + 
    BMI + CAD_history + Stroke_history + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)               Gendermale        Med.Statin.LLDyes                      BMI              CAD_history  
                0.16028                  0.59271                 -0.25963                  0.02923                  0.30318  
         Stroke_history            stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
                0.25028                 -1.09743                 -0.97086                 -0.65159                 -0.80087  
          stenose50-99%            stenose70-99%  
              -15.27559                  0.59126  

Degrees of Freedom: 998 Total (i.e. Null);  987 Residual
Null Deviance:      1331 
Residual Deviance: 1288     AIC: 1312

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9367  -1.2695   0.8101   0.9793   1.4492  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 0.134237   1.491544   0.090   0.9283    
currentDF[, PROTEIN]        0.054522   0.068254   0.799   0.4244    
Age                         0.001619   0.008307   0.195   0.8455    
Gendermale                  0.638876   0.146508   4.361  1.3e-05 ***
Hypertension.compositeyes  -0.108080   0.207201  -0.522   0.6019    
DiabetesStatusDiabetes     -0.123290   0.163538  -0.754   0.4509    
SmokerCurrentyes            0.124934   0.151554   0.824   0.4097    
Med.Statin.LLDyes          -0.249499   0.167274  -1.492   0.1358    
Med.all.antiplateletyes     0.161931   0.221057   0.733   0.4638    
GFR_MDRD                   -0.004974   0.003614  -1.376   0.1687    
BMI                         0.033028   0.018696   1.767   0.0773 .  
CAD_history                 0.310960   0.157200   1.978   0.0479 *  
Stroke_history              0.230621   0.146500   1.574   0.1154    
Peripheral.interv           0.037076   0.178283   0.208   0.8353    
stenose50-70%              -1.011322   1.165248  -0.868   0.3854    
stenose70-90%              -0.904406   1.139736  -0.794   0.4275    
stenose90-99%              -0.590355   1.139754  -0.518   0.6045    
stenose100% (Occlusion)    -0.735447   1.357428  -0.542   0.5880    
stenose50-99%             -15.239630 376.951258  -0.040   0.9678    
stenose70-99%               0.597045   1.580966   0.378   0.7057    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1331.0  on 998  degrees of freedom
Residual deviance: 1283.4  on 979  degrees of freedom
AIC: 1323.4

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.054522 
Standard error............: 0.068254 
Odds ratio (effect size)..: 1.056 
Lower 95% CI..............: 0.924 
Upper 95% CI..............: 1.207 
Z-value...................: 0.798812 
P-value...................: 0.4243993 
Hosmer and Lemeshow r^2...: 0.03579 
Cox and Snell r^2.........: 0.046565 
Nagelkerke's pseudo r^2...: 0.063256 
Sample size of AE DB......: 2388 
Sample size of model......: 999 
Missing data %............: 58.16583 

Analysis of IL6R_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Hypertension.composite + 
    DiabetesStatus + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)                        Age  Hypertension.compositeyes     DiabetesStatusDiabetes           SmokerCurrentyes  
                 -0.47387                    0.01628                    0.29007                   -0.24352                    0.33077  
              CAD_history              stenose50-70%              stenose70-90%              stenose90-99%    stenose100% (Occlusion)  
                  0.22888                   -1.57353                   -1.17165                   -0.93734                    0.08563  
            stenose50-99%              stenose70-99%  
                -15.69452                   -2.08325  

Degrees of Freedom: 1000 Total (i.e. Null);  989 Residual
Null Deviance:      1387 
Residual Deviance: 1354     AIC: 1378

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.5705  -1.1316  -0.8359   1.1617   1.7412  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                 0.212109   1.495726   0.142   0.8872  
currentDF[, PROTEIN]       -0.002449   0.066213  -0.037   0.9705  
Age                         0.013336   0.008150   1.636   0.1018  
Gendermale                 -0.162994   0.143965  -1.132   0.2576  
Hypertension.compositeyes   0.294333   0.200281   1.470   0.1417  
DiabetesStatusDiabetes     -0.249693   0.159825  -1.562   0.1182  
SmokerCurrentyes            0.336013   0.145847   2.304   0.0212 *
Med.Statin.LLDyes          -0.108235   0.161265  -0.671   0.5021  
Med.all.antiplateletyes    -0.255972   0.219348  -1.167   0.2432  
GFR_MDRD                   -0.002395   0.003551  -0.674   0.5000  
BMI                         0.004821   0.018465   0.261   0.7940  
CAD_history                 0.251978   0.149114   1.690   0.0911 .
Stroke_history             -0.166730   0.139636  -1.194   0.2325  
Peripheral.interv          -0.196876   0.172959  -1.138   0.2550  
stenose50-70%              -1.465448   1.195541  -1.226   0.2203  
stenose70-90%              -1.113704   1.168398  -0.953   0.3405  
stenose90-99%              -0.867558   1.168634  -0.742   0.4579  
stenose100% (Occlusion)     0.118872   1.429842   0.083   0.9337  
stenose50-99%             -15.589875 435.856093  -0.036   0.9715  
stenose70-99%              -1.997577   1.438041  -1.389   0.1648  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1386.7  on 1000  degrees of freedom
Residual deviance: 1347.6  on  981  degrees of freedom
AIC: 1387.6

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.002449 
Standard error............: 0.066213 
Odds ratio (effect size)..: 0.998 
Lower 95% CI..............: 0.876 
Upper 95% CI..............: 1.136 
Z-value...................: -0.036981 
P-value...................: 0.9704999 
Hosmer and Lemeshow r^2...: 0.028198 
Cox and Snell r^2.........: 0.03831 
Nagelkerke's pseudo r^2...: 0.051096 
Sample size of AE DB......: 2388 
Sample size of model......: 1001 
Missing data %............: 58.08208 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent + 
    CAD_history, family = binomial(link = "logit"), data = currentDF)

Coefficients:
     (Intercept)  SmokerCurrentyes       CAD_history  
          1.1251            0.4158            0.3017  

Degrees of Freedom: 1003 Total (i.e. Null);  1001 Residual
Null Deviance:      1022 
Residual Deviance: 1014     AIC: 1020

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1265   0.4975   0.6343   0.7205   0.9708  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.282e+01  7.188e+02   0.018  0.98577   
currentDF[, PROTEIN]       8.155e-03  8.142e-02   0.100  0.92022   
Age                        1.014e-02  9.909e-03   1.023  0.30614   
Gendermale                 4.618e-02  1.747e-01   0.264  0.79159   
Hypertension.compositeyes  2.825e-01  2.303e-01   1.227  0.21991   
DiabetesStatusDiabetes     2.034e-01  2.026e-01   1.004  0.31533   
SmokerCurrentyes           4.830e-01  1.850e-01   2.610  0.00905 **
Med.Statin.LLDyes          3.976e-02  1.953e-01   0.204  0.83865   
Med.all.antiplateletyes    2.293e-01  2.583e-01   0.888  0.37472   
GFR_MDRD                   4.130e-03  4.355e-03   0.948  0.34288   
BMI                        2.798e-02  2.331e-02   1.200  0.23000   
CAD_history                2.663e-01  1.871e-01   1.423  0.15476   
Stroke_history             2.061e-01  1.740e-01   1.184  0.23622   
Peripheral.interv          7.680e-02  2.159e-01   0.356  0.72202   
stenose50-70%             -1.365e+01  7.188e+02  -0.019  0.98485   
stenose70-90%             -1.411e+01  7.188e+02  -0.020  0.98434   
stenose90-99%             -1.411e+01  7.188e+02  -0.020  0.98434   
stenose100% (Occlusion)    3.208e-01  8.796e+02   0.000  0.99971   
stenose50-99%             -1.451e-01  1.020e+03   0.000  0.99989   
stenose70-99%             -1.370e+01  7.188e+02  -0.019  0.98479   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1021.76  on 1003  degrees of freedom
Residual deviance:  996.48  on  984  degrees of freedom
AIC: 1036.5

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.008155 
Standard error............: 0.081415 
Odds ratio (effect size)..: 1.008 
Lower 95% CI..............: 0.859 
Upper 95% CI..............: 1.183 
Z-value...................: 0.100161 
P-value...................: 0.9202161 
Hosmer and Lemeshow r^2...: 0.024745 
Cox and Snell r^2.........: 0.024868 
Nagelkerke's pseudo r^2...: 0.038943 
Sample size of AE DB......: 2388 
Sample size of model......: 1004 
Missing data %............: 57.95645 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale        Stroke_history     Peripheral.interv  
              0.4550                0.1229                0.7952                0.4442               -0.6211  

Degrees of Freedom: 1003 Total (i.e. Null);  999 Residual
Null Deviance:      1171 
Residual Deviance: 1122     AIC: 1132

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0862  -1.1264   0.6590   0.7969   1.8542  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.332e+01  4.411e+02   0.030  0.97591    
currentDF[, PROTEIN]       9.800e-02  7.527e-02   1.302  0.19292    
Age                        1.132e-02  9.265e-03   1.222  0.22178    
Gendermale                 8.405e-01  1.583e-01   5.308 1.11e-07 ***
Hypertension.compositeyes -1.069e-03  2.299e-01  -0.005  0.99629    
DiabetesStatusDiabetes    -1.151e-01  1.801e-01  -0.639  0.52259    
SmokerCurrentyes           1.497e-01  1.676e-01   0.894  0.37152    
Med.Statin.LLDyes         -1.464e-01  1.904e-01  -0.769  0.44205    
Med.all.antiplateletyes    4.523e-02  2.493e-01   0.181  0.85603    
GFR_MDRD                   5.281e-06  4.114e-03   0.001  0.99898    
BMI                       -5.235e-03  2.075e-02  -0.252  0.80084    
CAD_history               -6.362e-02  1.695e-01  -0.375  0.70743    
Stroke_history             4.412e-01  1.660e-01   2.658  0.00787 ** 
Peripheral.interv         -5.960e-01  1.843e-01  -3.234  0.00122 ** 
stenose50-70%             -1.358e+01  4.411e+02  -0.031  0.97545    
stenose70-90%             -1.349e+01  4.411e+02  -0.031  0.97560    
stenose90-99%             -1.338e+01  4.411e+02  -0.030  0.97581    
stenose100% (Occlusion)   -1.416e+01  4.411e+02  -0.032  0.97440    
stenose50-99%             -1.586e+01  4.411e+02  -0.036  0.97132    
stenose70-99%             -1.470e+01  4.411e+02  -0.033  0.97341    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1171.0  on 1003  degrees of freedom
Residual deviance: 1106.8  on  984  degrees of freedom
AIC: 1146.8

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.097997 
Standard error............: 0.075268 
Odds ratio (effect size)..: 1.103 
Lower 95% CI..............: 0.952 
Upper 95% CI..............: 1.278 
Z-value...................: 1.301985 
P-value...................: 0.1929215 
Hosmer and Lemeshow r^2...: 0.054819 
Cox and Snell r^2.........: 0.061938 
Nagelkerke's pseudo r^2...: 0.08996 
Sample size of AE DB......: 2388 
Sample size of model......: 1004 
Missing data %............: 57.95645 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + GFR_MDRD + CAD_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale              GFR_MDRD           CAD_history  
            0.347137              0.149195              0.581414             -0.004984              0.228735  

Degrees of Freedom: 1001 Total (i.e. Null);  997 Residual
Null Deviance:      1338 
Residual Deviance: 1311     AIC: 1321

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9319  -1.2821   0.8263   0.9878   1.4773  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)                0.693593   1.522867   0.455   0.6488    
currentDF[, PROTEIN]       0.136420   0.068451   1.993   0.0463 *  
Age                       -0.001134   0.008382  -0.135   0.8924    
Gendermale                 0.611644   0.146076   4.187 2.82e-05 ***
Hypertension.compositeyes -0.110197   0.204287  -0.539   0.5896    
DiabetesStatusDiabetes    -0.154479   0.162386  -0.951   0.3414    
SmokerCurrentyes           0.113081   0.150460   0.752   0.4523    
Med.Statin.LLDyes         -0.148415   0.167690  -0.885   0.3761    
Med.all.antiplateletyes    0.060511   0.223809   0.270   0.7869    
GFR_MDRD                  -0.005323   0.003674  -1.449   0.1474    
BMI                        0.017774   0.018965   0.937   0.3486    
CAD_history                0.289601   0.155217   1.866   0.0621 .  
Stroke_history             0.153900   0.144197   1.067   0.2858    
Peripheral.interv          0.004085   0.178235   0.023   0.9817    
stenose50-70%             -0.739569   1.207855  -0.612   0.5403    
stenose70-90%             -0.772461   1.182418  -0.653   0.5136    
stenose90-99%             -0.519486   1.182942  -0.439   0.6606    
stenose100% (Occlusion)   -0.605786   1.393142  -0.435   0.6637    
stenose50-99%             -1.096639   1.556511  -0.705   0.4811    
stenose70-99%              0.852030   1.607425   0.530   0.5961    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1337.7  on 1001  degrees of freedom
Residual deviance: 1299.2  on  982  degrees of freedom
AIC: 1339.2

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.13642 
Standard error............: 0.068451 
Odds ratio (effect size)..: 1.146 
Lower 95% CI..............: 1.002 
Upper 95% CI..............: 1.311 
Z-value...................: 1.992945 
P-value...................: 0.04626743 
Hosmer and Lemeshow r^2...: 0.028723 
Cox and Snell r^2.........: 0.037619 
Nagelkerke's pseudo r^2...: 0.051055 
Sample size of AE DB......: 2388 
Sample size of model......: 1002 
Missing data %............: 58.0402 

Analysis of MCP1_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age         SmokerCurrentyes              CAD_history  
               -1.37299                 -0.47406                  0.02058                  0.40394                  0.24014  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%  
               -0.88314                 -0.37300                 -0.18504                  0.71187                -14.80113  
          stenose70-99%  
               -1.21694  

Degrees of Freedom: 1038 Total (i.e. Null);  1028 Residual
Null Deviance:      1439 
Residual Deviance: 1354     AIC: 1376

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8920  -1.0857  -0.6854   1.1100   2.0741  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                -1.111475   1.329496  -0.836  0.40315    
currentDF[, PROTEIN]       -0.474349   0.068483  -6.927 4.31e-12 ***
Age                         0.017814   0.008172   2.180  0.02927 *  
Gendermale                 -0.141964   0.144258  -0.984  0.32507    
Hypertension.compositeyes   0.223348   0.202733   1.102  0.27060    
DiabetesStatusDiabetes     -0.239143   0.160905  -1.486  0.13722    
SmokerCurrentyes            0.415158   0.147167   2.821  0.00479 ** 
Med.Statin.LLDyes          -0.180621   0.161314  -1.120  0.26285    
Med.all.antiplateletyes    -0.196875   0.219018  -0.899  0.36871    
GFR_MDRD                   -0.001495   0.003520  -0.425  0.67110    
BMI                         0.012799   0.018157   0.705  0.48087    
CAD_history                 0.263913   0.150066   1.759  0.07864 .  
Stroke_history             -0.113515   0.140974  -0.805  0.42069    
Peripheral.interv          -0.192409   0.172486  -1.116  0.26463    
stenose50-70%              -0.816931   0.965795  -0.846  0.39763    
stenose70-90%              -0.342436   0.930578  -0.368  0.71289    
stenose90-99%              -0.154316   0.930195  -0.166  0.86824    
stenose100% (Occlusion)     0.718982   1.255767   0.573  0.56695    
stenose50-99%             -14.791163 417.958374  -0.035  0.97177    
stenose70-99%              -1.145147   1.267273  -0.904  0.36619    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1439.2  on 1038  degrees of freedom
Residual deviance: 1345.5  on 1019  degrees of freedom
AIC: 1385.5

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.474349 
Standard error............: 0.068483 
Odds ratio (effect size)..: 0.622 
Lower 95% CI..............: 0.544 
Upper 95% CI..............: 0.712 
Z-value...................: -6.926517 
P-value...................: 4.313271e-12 
Hosmer and Lemeshow r^2...: 0.065099 
Cox and Snell r^2.........: 0.086227 
Nagelkerke's pseudo r^2...: 0.115013 
Sample size of AE DB......: 2388 
Sample size of model......: 1039 
Missing data %............: 56.49079 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerCurrent + BMI, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]      SmokerCurrentyes                   BMI  
             0.20733              -0.23235               0.44405               0.03906  

Degrees of Freedom: 1041 Total (i.e. Null);  1038 Residual
Null Deviance:      1055 
Residual Deviance: 1037     AIC: 1045

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2648   0.4510   0.6178   0.7190   1.0442  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.346e+01  1.061e+03   0.013  0.98988   
currentDF[, PROTEIN]      -2.260e-01  7.941e-02  -2.846  0.00443 **
Age                        1.026e-02  9.723e-03   1.055  0.29133   
Gendermale                 2.043e-02  1.727e-01   0.118  0.90585   
Hypertension.compositeyes  2.367e-01  2.301e-01   1.029  0.30368   
DiabetesStatusDiabetes     1.304e-01  1.981e-01   0.658  0.51029   
SmokerCurrentyes           5.189e-01  1.836e-01   2.826  0.00472 **
Med.Statin.LLDyes          1.422e-02  1.924e-01   0.074  0.94111   
Med.all.antiplateletyes    1.565e-01  2.563e-01   0.611  0.54149   
GFR_MDRD                   5.598e-03  4.241e-03   1.320  0.18688   
BMI                        4.070e-02  2.308e-02   1.763  0.07787 . 
CAD_history                2.143e-01  1.838e-01   1.166  0.24353   
Stroke_history             2.224e-01  1.727e-01   1.288  0.19786   
Peripheral.interv          1.098e-01  2.127e-01   0.516  0.60574   
stenose50-70%             -1.453e+01  1.061e+03  -0.014  0.98907   
stenose70-90%             -1.499e+01  1.061e+03  -0.014  0.98873   
stenose90-99%             -1.505e+01  1.061e+03  -0.014  0.98869   
stenose100% (Occlusion)    2.439e-01  1.351e+03   0.000  0.99986   
stenose50-99%             -1.824e-02  1.589e+03   0.000  0.99999   
stenose70-99%             -1.449e+01  1.061e+03  -0.014  0.98911   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1055.5  on 1041  degrees of freedom
Residual deviance: 1019.7  on 1022  degrees of freedom
AIC: 1059.7

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.225966 
Standard error............: 0.079411 
Odds ratio (effect size)..: 0.798 
Lower 95% CI..............: 0.683 
Upper 95% CI..............: 0.932 
Z-value...................: -2.845529 
P-value...................: 0.00443377 
Hosmer and Lemeshow r^2...: 0.033878 
Cox and Snell r^2.........: 0.033733 
Nagelkerke's pseudo r^2...: 0.052969 
Sample size of AE DB......: 2388 
Sample size of model......: 1042 
Missing data %............: 56.36516 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]               Gendermale           Stroke_history        Peripheral.interv  
                13.9001                   0.1685                   0.8324                   0.4438                  -0.6179  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%  
               -13.6499                 -13.5208                 -13.3630                 -14.0674                 -15.9302  
          stenose70-99%  
               -14.7641  

Degrees of Freedom: 1041 Total (i.e. Null);  1031 Residual
Null Deviance:      1225 
Residual Deviance: 1154     AIC: 1176

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1038  -1.1336   0.6504   0.8116   1.7658  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.325e+01  3.926e+02   0.034 0.973076    
currentDF[, PROTEIN]       1.633e-01  7.323e-02   2.230 0.025771 *  
Age                        7.988e-03  8.972e-03   0.890 0.373294    
Gendermale                 8.706e-01  1.541e-01   5.650  1.6e-08 ***
Hypertension.compositeyes  4.406e-02  2.265e-01   0.194 0.845801    
DiabetesStatusDiabetes    -1.615e-01  1.756e-01  -0.920 0.357650    
SmokerCurrentyes           1.448e-01  1.637e-01   0.885 0.376349    
Med.Statin.LLDyes         -1.876e-01  1.861e-01  -1.008 0.313562    
Med.all.antiplateletyes    7.910e-02  2.423e-01   0.327 0.744039    
GFR_MDRD                  -6.075e-04  3.926e-03  -0.155 0.877040    
BMI                        3.774e-03  1.972e-02   0.191 0.848251    
CAD_history               -3.938e-02  1.660e-01  -0.237 0.812467    
Stroke_history             4.320e-01  1.633e-01   2.645 0.008160 ** 
Peripheral.interv         -5.908e-01  1.790e-01  -3.300 0.000965 ***
stenose50-70%             -1.358e+01  3.926e+02  -0.035 0.972398    
stenose70-90%             -1.345e+01  3.926e+02  -0.034 0.972660    
stenose90-99%             -1.331e+01  3.926e+02  -0.034 0.972956    
stenose100% (Occlusion)   -1.405e+01  3.926e+02  -0.036 0.971459    
stenose50-99%             -1.591e+01  3.926e+02  -0.041 0.967674    
stenose70-99%             -1.478e+01  3.926e+02  -0.038 0.969972    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1224.7  on 1041  degrees of freedom
Residual deviance: 1150.4  on 1022  degrees of freedom
AIC: 1190.4

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.163285 
Standard error............: 0.073234 
Odds ratio (effect size)..: 1.177 
Lower 95% CI..............: 1.02 
Upper 95% CI..............: 1.359 
Z-value...................: 2.22964 
P-value...................: 0.02577136 
Hosmer and Lemeshow r^2...: 0.060666 
Cox and Snell r^2.........: 0.068819 
Nagelkerke's pseudo r^2...: 0.099553 
Sample size of AE DB......: 2388 
Sample size of model......: 1042 
Missing data %............: 56.36516 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Med.Statin.LLD + BMI + CAD_history + Stroke_history, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale     Med.Statin.LLDyes                   BMI           CAD_history  
            -0.57216              -0.13871               0.54948              -0.27048               0.02716               0.26512  
      Stroke_history  
             0.24072  

Degrees of Freedom: 1039 Total (i.e. Null);  1033 Residual
Null Deviance:      1389 
Residual Deviance: 1357     AIC: 1371

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9631  -1.2808   0.8129   0.9902   1.5437  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                0.2859982  1.4840343   0.193   0.8472    
currentDF[, PROTEIN]      -0.1379693  0.0658565  -2.095   0.0362 *  
Age                        0.0001424  0.0081461   0.017   0.9860    
Gendermale                 0.6169887  0.1429940   4.315  1.6e-05 ***
Hypertension.compositeyes -0.1347125  0.2023482  -0.666   0.5056    
DiabetesStatusDiabetes    -0.1318405  0.1596422  -0.826   0.4089    
SmokerCurrentyes           0.1353045  0.1476443   0.916   0.3594    
Med.Statin.LLDyes         -0.2582250  0.1645153  -1.570   0.1165    
Med.all.antiplateletyes    0.1499145  0.2180924   0.687   0.4918    
GFR_MDRD                  -0.0048941  0.0035333  -1.385   0.1660    
BMI                        0.0318061  0.0183315   1.735   0.0827 .  
CAD_history                0.2693138  0.1520944   1.771   0.0766 .  
Stroke_history             0.2396462  0.1427314   1.679   0.0932 .  
Peripheral.interv          0.0531500  0.1737461   0.306   0.7597    
stenose50-70%             -0.9815249  1.1707234  -0.838   0.4018    
stenose70-90%             -0.8530624  1.1451377  -0.745   0.4563    
stenose90-99%             -0.5996543  1.1451261  -0.524   0.6005    
stenose100% (Occlusion)   -0.7495926  1.3619788  -0.550   0.5821    
stenose50-99%             -1.2353440  1.5277375  -0.809   0.4187    
stenose70-99%              0.6521140  1.5833657   0.412   0.6804    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1388.6  on 1039  degrees of freedom
Residual deviance: 1344.4  on 1020  degrees of freedom
AIC: 1384.4

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: -0.137969 
Standard error............: 0.065856 
Odds ratio (effect size)..: 0.871 
Lower 95% CI..............: 0.766 
Upper 95% CI..............: 0.991 
Z-value...................: -2.095 
P-value...................: 0.03617101 
Hosmer and Lemeshow r^2...: 0.031873 
Cox and Snell r^2.........: 0.041665 
Nagelkerke's pseudo r^2...: 0.056541 
Sample size of AE DB......: 2388 
Sample size of model......: 1040 
Missing data %............: 56.44891 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 3

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, stenosis, and LDL.

Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_LN.

- processing Macrophages_LN
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(COVARIATES_M3)` instead of `COVARIATES_M3` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.

Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
          4  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9759 -0.7033 -0.0034  0.6905  4.2713 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                7.740947   1.579448   4.901 1.68e-06 ***
currentDF[, TRAIT]        -0.025743   0.034058  -0.756   0.4504    
Age                       -0.004751   0.009140  -0.520   0.6037    
Gendermale                -0.272080   0.157730  -1.725   0.0857 .  
Hypertension.compositeyes  0.037631   0.209430   0.180   0.8575    
DiabetesStatusDiabetes    -0.090890   0.182293  -0.499   0.6185    
SmokerCurrentyes           0.081890   0.146903   0.557   0.5777    
Med.Statin.LLDyes         -0.249783   0.162381  -1.538   0.1252    
Med.all.antiplateletyes   -0.239031   0.281515  -0.849   0.3966    
GFR_MDRD                   0.004160   0.004033   1.031   0.3033    
BMI                       -0.010546   0.018853  -0.559   0.5764    
CAD_history                0.175663   0.150932   1.164   0.2456    
Stroke_history             0.078636   0.144235   0.545   0.5861    
Peripheral.interv         -0.149698   0.168586  -0.888   0.3754    
stenose50-70%             -2.622359   1.182113  -2.218   0.0274 *  
stenose70-90%             -2.484979   1.121013  -2.217   0.0275 *  
stenose90-99%             -2.587512   1.120320  -2.310   0.0217 *  
stenose100% (Occlusion)   -3.253230   1.268663  -2.564   0.0109 *  
LDL_final                 -0.144230   0.071090  -2.029   0.0435 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.093 on 259 degrees of freedom
Multiple R-squared:  0.06051,   Adjusted R-squared:  -0.004781 
F-statistic: 0.9268 on 18 and 259 DF,  p-value: 0.5466

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: -0.025743 
Standard error............: 0.034058 
Odds ratio (effect size)..: 0.975 
Lower 95% CI..............: 0.912 
Upper 95% CI..............: 1.042 
T-value...................: -0.75585 
P-value...................: 0.4504262 
R^2.......................: 0.060512 
Adjusted r^2..............: -0.004781 
Sample size of AE DB......: 2388 
Sample size of model......: 278 
Missing data %............: 88.35846 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ 1, data = currentDF)

Coefficients:
(Intercept)  
       4.02  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0261 -0.6910  0.0053  0.6562  4.2703 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                8.045022   1.559939   5.157 4.94e-07 ***
currentDF[, TRAIT]         0.006083   0.044333   0.137   0.8910    
Age                       -0.007760   0.008928  -0.869   0.3856    
Gendermale                -0.256524   0.155255  -1.652   0.0997 .  
Hypertension.compositeyes -0.003596   0.198882  -0.018   0.9856    
DiabetesStatusDiabetes    -0.118621   0.178749  -0.664   0.5075    
SmokerCurrentyes           0.002381   0.143389   0.017   0.9868    
Med.Statin.LLDyes         -0.219813   0.159597  -1.377   0.1696    
Med.all.antiplateletyes   -0.210099   0.279758  -0.751   0.4533    
GFR_MDRD                   0.003900   0.004023   0.969   0.3333    
BMI                       -0.017825   0.018090  -0.985   0.3254    
CAD_history                0.136209   0.149997   0.908   0.3647    
Stroke_history             0.057146   0.141989   0.402   0.6877    
Peripheral.interv         -0.073426   0.169840  -0.432   0.6659    
stenose50-70%             -2.558450   1.175260  -2.177   0.0304 *  
stenose70-90%             -2.413938   1.114671  -2.166   0.0312 *  
stenose90-99%             -2.467474   1.112675  -2.218   0.0274 *  
stenose100% (Occlusion)   -3.172581   1.259270  -2.519   0.0123 *  
LDL_final                 -0.119656   0.071964  -1.663   0.0976 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.086 on 263 degrees of freedom
Multiple R-squared:  0.05194,   Adjusted R-squared:  -0.01295 
F-statistic: 0.8004 on 18 and 263 DF,  p-value: 0.6994

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: 0.006083 
Standard error............: 0.044333 
Odds ratio (effect size)..: 1.006 
Lower 95% CI..............: 0.922 
Upper 95% CI..............: 1.097 
T-value...................: 0.137207 
P-value...................: 0.8909723 
R^2.......................: 0.051935 
Adjusted r^2..............: -0.012951 
Sample size of AE DB......: 2388 
Sample size of model......: 282 
Missing data %............: 88.19096 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD + LDL_final, data = currentDF)

Coefficients:
(Intercept)     GFR_MDRD    LDL_final  
   3.925179     0.005269    -0.093708  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9394 -0.6969 -0.0003  0.6700  4.3763 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                8.186084   1.618862   5.057 8.14e-07 ***
currentDF[, TRAIT]        -0.071900   0.102862  -0.699   0.4852    
Age                       -0.009714   0.009121  -1.065   0.2879    
Gendermale                -0.207723   0.157206  -1.321   0.1876    
Hypertension.compositeyes  0.032540   0.206556   0.158   0.8749    
DiabetesStatusDiabetes    -0.093555   0.187795  -0.498   0.6188    
SmokerCurrentyes           0.027341   0.147137   0.186   0.8527    
Med.Statin.LLDyes         -0.210534   0.161866  -1.301   0.1945    
Med.all.antiplateletyes   -0.127005   0.297437  -0.427   0.6697    
GFR_MDRD                   0.004613   0.004119   1.120   0.2638    
BMI                       -0.017017   0.018372  -0.926   0.3552    
CAD_history                0.158145   0.153266   1.032   0.3031    
Stroke_history             0.070817   0.143918   0.492   0.6231    
Peripheral.interv         -0.087139   0.171114  -0.509   0.6110    
stenose50-70%             -2.634075   1.189079  -2.215   0.0276 *  
stenose70-90%             -2.461712   1.128624  -2.181   0.0301 *  
stenose90-99%             -2.542068   1.126823  -2.256   0.0249 *  
stenose100% (Occlusion)   -2.745285   1.332360  -2.060   0.0404 *  
LDL_final                 -0.139731   0.071674  -1.950   0.0523 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.096 on 256 degrees of freedom
Multiple R-squared:  0.05537,   Adjusted R-squared:  -0.01105 
F-statistic: 0.8337 on 18 and 256 DF,  p-value: 0.6596

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.0719 
Standard error............: 0.102862 
Odds ratio (effect size)..: 0.931 
Lower 95% CI..............: 0.761 
Upper 95% CI..............: 1.138 
T-value...................: -0.698998 
P-value...................: 0.4851877 
R^2.......................: 0.055374 
Adjusted r^2..............: -0.011045 
Sample size of AE DB......: 2388 
Sample size of model......: 275 
Missing data %............: 88.48409 

Analysis of MCP1_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + Med.Statin.LLD + CAD_history, 
    data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale  Hypertension.compositeyes  
                 5.433871                   0.052907                  -0.008419                   0.283822                  -0.246632  
        Med.Statin.LLDyes                CAD_history  
                -0.245394                   0.148189  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3408 -0.5120  0.1051  0.5632  1.9179 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                6.575157   1.149922   5.718 2.42e-08 ***
currentDF[, TRAIT]         0.051202   0.024271   2.110  0.03565 *  
Age                       -0.010344   0.006135  -1.686  0.09275 .  
Gendermale                 0.283093   0.108417   2.611  0.00944 ** 
Hypertension.compositeyes -0.233293   0.140032  -1.666  0.09667 .  
DiabetesStatusDiabetes    -0.052638   0.125203  -0.420  0.67445    
SmokerCurrentyes          -0.070866   0.100373  -0.706  0.48067    
Med.Statin.LLDyes         -0.200910   0.114627  -1.753  0.08058 .  
Med.all.antiplateletyes    0.037672   0.198929   0.189  0.84992    
GFR_MDRD                  -0.001374   0.002743  -0.501  0.61668    
BMI                       -0.009776   0.012528  -0.780  0.43574    
CAD_history                0.180157   0.106974   1.684  0.09311 .  
Stroke_history             0.050938   0.100817   0.505  0.61372    
Peripheral.interv         -0.146160   0.121478  -1.203  0.22977    
stenose50-70%             -0.799688   0.914165  -0.875  0.38234    
stenose70-90%             -0.882827   0.875230  -1.009  0.31387    
stenose90-99%             -0.864213   0.875215  -0.987  0.32416    
stenose100% (Occlusion)   -1.846010   0.984886  -1.874  0.06177 .  
LDL_final                  0.059863   0.048325   1.239  0.21632    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8575 on 329 degrees of freedom
Multiple R-squared:  0.09616,   Adjusted R-squared:  0.04671 
F-statistic: 1.945 on 18 and 329 DF,  p-value: 0.01237

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0.051202 
Standard error............: 0.024271 
Odds ratio (effect size)..: 1.053 
Lower 95% CI..............: 1.004 
Upper 95% CI..............: 1.104 
T-value...................: 2.10957 
P-value...................: 0.03565039 
R^2.......................: 0.096164 
Adjusted r^2..............: 0.046714 
Sample size of AE DB......: 2388 
Sample size of model......: 348 
Missing data %............: 85.42714 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + CAD_history + LDL_final, 
    data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale  Hypertension.compositeyes  
                  4.91085                   -0.11828                   -0.01118                    0.31926                   -0.20731  
              CAD_history                  LDL_final  
                  0.20467                    0.14057  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2139 -0.5490  0.0567  0.5531  1.8562 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                6.577965   1.115427   5.897 9.08e-09 ***
currentDF[, TRAIT]        -0.115466   0.029928  -3.858 0.000137 ***
Age                       -0.014634   0.005906  -2.478 0.013716 *  
Gendermale                 0.293683   0.104952   2.798 0.005437 ** 
Hypertension.compositeyes -0.219243   0.132334  -1.657 0.098514 .  
DiabetesStatusDiabetes    -0.037367   0.121647  -0.307 0.758903    
SmokerCurrentyes          -0.061230   0.096647  -0.634 0.526814    
Med.Statin.LLDyes         -0.135829   0.110810  -1.226 0.221149    
Med.all.antiplateletyes    0.015496   0.193719   0.080 0.936290    
GFR_MDRD                  -0.001360   0.002667  -0.510 0.610531    
BMI                       -0.006503   0.011921  -0.546 0.585774    
CAD_history                0.229533   0.104305   2.201 0.028450 *  
Stroke_history             0.072505   0.097399   0.744 0.457152    
Peripheral.interv         -0.141222   0.119345  -1.183 0.237528    
stenose50-70%             -0.850207   0.890508  -0.955 0.340400    
stenose70-90%             -0.964299   0.852952  -1.131 0.259061    
stenose90-99%             -0.945819   0.852072  -1.110 0.267791    
stenose100% (Occlusion)   -1.982611   0.958701  -2.068 0.039410 *  
LDL_final                  0.114283   0.048010   2.380 0.017856 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8357 on 333 degrees of freedom
Multiple R-squared:  0.1366,    Adjusted R-squared:  0.0899 
F-statistic: 2.926 on 18 and 333 DF,  p-value: 7.408e-05

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.115466 
Standard error............: 0.029928 
Odds ratio (effect size)..: 0.891 
Lower 95% CI..............: 0.84 
Upper 95% CI..............: 0.945 
T-value...................: -3.858177 
P-value...................: 0.0001371672 
R^2.......................: 0.136572 
Adjusted r^2..............: 0.0899 
Sample size of AE DB......: 2388 
Sample size of model......: 352 
Missing data %............: 85.25963 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + Hypertension.composite + 
    Med.Statin.LLD + CAD_history, data = currentDF)

Coefficients:
              (Intercept)                        Age                 Gendermale  Hypertension.compositeyes          Med.Statin.LLDyes  
                 5.411787                  -0.009824                   0.346534                  -0.231090                  -0.247329  
              CAD_history  
                 0.171992  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3025 -0.5216  0.0801  0.5662  1.9158 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                6.5990903  1.1774762   5.604 4.44e-08 ***
currentDF[, TRAIT]        -0.0452791  0.0674970  -0.671  0.50280    
Age                       -0.0109509  0.0060958  -1.796  0.07334 .  
Gendermale                 0.3402105  0.1089102   3.124  0.00195 ** 
Hypertension.compositeyes -0.2187995  0.1395317  -1.568  0.11783    
DiabetesStatusDiabetes    -0.0149288  0.1275963  -0.117  0.90693    
SmokerCurrentyes          -0.0647504  0.1007582  -0.643  0.52091    
Med.Statin.LLDyes         -0.2091982  0.1151857  -1.816  0.07026 .  
Med.all.antiplateletyes    0.0262592  0.2037972   0.129  0.89756    
GFR_MDRD                  -0.0004959  0.0027971  -0.177  0.85939    
BMI                       -0.0081593  0.0124271  -0.657  0.51192    
CAD_history                0.1953042  0.1090301   1.791  0.07417 .  
Stroke_history             0.0600427  0.1009723   0.595  0.55249    
Peripheral.interv         -0.1222098  0.1239640  -0.986  0.32494    
stenose50-70%             -0.9307527  0.9190688  -1.013  0.31195    
stenose70-90%             -0.9661136  0.8811480  -1.096  0.27370    
stenose90-99%             -0.9411412  0.8803146  -1.069  0.28581    
stenose100% (Occlusion)   -1.9654537  0.9917186  -1.982  0.04833 *  
LDL_final                  0.0510253  0.0491298   1.039  0.29977    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8609 on 327 degrees of freedom
Multiple R-squared:  0.09072,   Adjusted R-squared:  0.04067 
F-statistic: 1.813 on 18 and 327 DF,  p-value: 0.0229

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.045279 
Standard error............: 0.067497 
Odds ratio (effect size)..: 0.956 
Lower 95% CI..............: 0.837 
Upper 95% CI..............: 1.091 
T-value...................: -0.670831 
P-value...................: 0.5028019 
R^2.......................: 0.090722 
Adjusted r^2..............: 0.04067 
Sample size of AE DB......: 2388 
Sample size of model......: 346 
Missing data %............: 85.51089 

Analysis of IL6_pg_ug_2015_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + SmokerCurrent + 
    BMI + Stroke_history, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]    SmokerCurrentyes                 BMI      Stroke_history  
          -2.43206             0.09645             0.21278            -0.02995             0.32429  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.8604 -0.9047  0.0230  0.8359  4.8386 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -1.557747   1.218463  -1.278  0.20159   
currentDF[, TRAIT]         0.094228   0.033001   2.855  0.00445 **
Age                       -0.005006   0.007368  -0.679  0.49711   
Gendermale                -0.046955   0.133198  -0.353  0.72458   
Hypertension.compositeyes -0.122887   0.185203  -0.664  0.50725   
DiabetesStatusDiabetes    -0.095874   0.149517  -0.641  0.52163   
SmokerCurrentyes           0.199220   0.129973   1.533  0.12586   
Med.Statin.LLDyes         -0.133702   0.151115  -0.885  0.37664   
Med.all.antiplateletyes   -0.032443   0.210849  -0.154  0.87777   
GFR_MDRD                  -0.005327   0.003190  -1.670  0.09546 . 
BMI                       -0.033230   0.016260  -2.044  0.04143 * 
CAD_history               -0.080693   0.137498  -0.587  0.55752   
Stroke_history             0.294319   0.129450   2.274  0.02335 * 
Peripheral.interv         -0.073667   0.157291  -0.468  0.63971   
stenose50-70%              0.043122   0.896119   0.048  0.96164   
stenose70-90%              0.421831   0.854836   0.493  0.62187   
stenose90-99%              0.194343   0.853986   0.228  0.82006   
stenose100% (Occlusion)    0.199915   1.046672   0.191  0.84859   
stenose70-99%             -1.262844   1.353912  -0.933  0.35134   
LDL_final                 -0.002720   0.060284  -0.045  0.96403   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.46 on 590 degrees of freedom
Multiple R-squared:  0.05558,   Adjusted R-squared:  0.02517 
F-statistic: 1.828 on 19 and 590 DF,  p-value: 0.01729

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0.094228 
Standard error............: 0.033001 
Odds ratio (effect size)..: 1.099 
Lower 95% CI..............: 1.03 
Upper 95% CI..............: 1.172 
T-value...................: 2.85531 
P-value...................: 0.004450697 
R^2.......................: 0.055584 
Adjusted r^2..............: 0.025171 
Sample size of AE DB......: 2388 
Sample size of model......: 610 
Missing data %............: 74.45561 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + SmokerCurrent + 
    BMI + Stroke_history, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]    SmokerCurrentyes                 BMI      Stroke_history  
          -2.70073            -0.16624             0.24106            -0.02353             0.27785  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.5367 -0.9049  0.0226  0.8649  4.3823 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -1.655561   1.203986  -1.375   0.1696    
currentDF[, TRAIT]        -0.172293   0.041530  -4.149 3.83e-05 ***
Age                       -0.010475   0.007294  -1.436   0.1515    
Gendermale                -0.080710   0.132821  -0.608   0.5436    
Hypertension.compositeyes -0.059646   0.180561  -0.330   0.7413    
DiabetesStatusDiabetes    -0.078541   0.148335  -0.529   0.5967    
SmokerCurrentyes           0.195491   0.128526   1.521   0.1288    
Med.Statin.LLDyes         -0.128654   0.148731  -0.865   0.3874    
Med.all.antiplateletyes   -0.047780   0.207507  -0.230   0.8180    
GFR_MDRD                  -0.004567   0.003173  -1.440   0.1505    
BMI                       -0.027125   0.015913  -1.705   0.0888 .  
CAD_history               -0.079941   0.136513  -0.586   0.5584    
Stroke_history             0.268793   0.128036   2.099   0.0362 *  
Peripheral.interv         -0.051546   0.157517  -0.327   0.7436    
stenose50-70%              0.064469   0.888583   0.073   0.9422    
stenose70-90%              0.480430   0.847963   0.567   0.5712    
stenose90-99%              0.261853   0.846882   0.309   0.7573    
stenose100% (Occlusion)    0.357215   1.010357   0.354   0.7238    
stenose70-99%             -0.949383   1.343326  -0.707   0.4800    
LDL_final                  0.021597   0.060074   0.360   0.7193    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.448 on 593 degrees of freedom
Multiple R-squared:  0.06707,   Adjusted R-squared:  0.03717 
F-statistic: 2.244 on 19 and 593 DF,  p-value: 0.001915

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.172293 
Standard error............: 0.04153 
Odds ratio (effect size)..: 0.842 
Lower 95% CI..............: 0.776 
Upper 95% CI..............: 0.913 
T-value...................: -4.148622 
P-value...................: 3.834173e-05 
R^2.......................: 0.067066 
Adjusted r^2..............: 0.037174 
Sample size of AE DB......: 2388 
Sample size of model......: 613 
Missing data %............: 74.32998 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ SmokerCurrent + GFR_MDRD + 
    BMI + Stroke_history, data = currentDF)

Coefficients:
     (Intercept)  SmokerCurrentyes          GFR_MDRD               BMI    Stroke_history  
       -2.344005          0.259757         -0.004498         -0.028325          0.352416  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.7750 -0.8639  0.0090  0.8596  4.6815 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -1.790409   1.260654  -1.420   0.1561  
currentDF[, TRAIT]        -0.066015   0.076053  -0.868   0.3858  
Age                       -0.003151   0.007664  -0.411   0.6812  
Gendermale                -0.009092   0.138905  -0.065   0.9478  
Hypertension.compositeyes -0.118670   0.194865  -0.609   0.5428  
DiabetesStatusDiabetes    -0.044101   0.160394  -0.275   0.7835  
SmokerCurrentyes           0.234773   0.136162   1.724   0.0852 .
Med.Statin.LLDyes         -0.119714   0.158225  -0.757   0.4496  
Med.all.antiplateletyes   -0.042429   0.229876  -0.185   0.8536  
GFR_MDRD                  -0.005754   0.003392  -1.696   0.0904 .
BMI                       -0.029793   0.016765  -1.777   0.0761 .
CAD_history               -0.091576   0.146623  -0.625   0.5325  
Stroke_history             0.324994   0.136565   2.380   0.0177 *
Peripheral.interv         -0.092246   0.169383  -0.545   0.5863  
stenose50-70%             -0.074196   0.908935  -0.082   0.9350  
stenose70-90%              0.295609   0.863719   0.342   0.7323  
stenose90-99%              0.102435   0.862536   0.119   0.9055  
stenose100% (Occlusion)    0.128583   1.056678   0.122   0.9032  
stenose70-99%             -1.344224   1.735406  -0.775   0.4389  
LDL_final                  0.016792   0.065246   0.257   0.7970  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.471 on 542 degrees of freedom
Multiple R-squared:  0.04595,   Adjusted R-squared:  0.01251 
F-statistic: 1.374 on 19 and 542 DF,  p-value: 0.1332

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.066015 
Standard error............: 0.076053 
Odds ratio (effect size)..: 0.936 
Lower 95% CI..............: 0.806 
Upper 95% CI..............: 1.087 
T-value...................: -0.868014 
P-value...................: 0.3857707 
R^2.......................: 0.045953 
Adjusted r^2..............: 0.012509 
Sample size of AE DB......: 2388 
Sample size of model......: 562 
Missing data %............: 76.46566 

Analysis of IL6R_pg_ug_2015_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    DiabetesStatus + Med.Statin.LLD + GFR_MDRD + Peripheral.interv, 
    data = currentDF)

Coefficients:
           (Intercept)      currentDF[, TRAIT]                     Age  DiabetesStatusDiabetes       Med.Statin.LLDyes  
             -0.758425                0.061200               -0.007761               -0.173464               -0.263146  
              GFR_MDRD       Peripheral.interv  
             -0.004980                0.315321  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.5997 -0.4916  0.1033  0.6403  2.7135 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -0.282532   1.033781  -0.273  0.78472   
currentDF[, TRAIT]         0.064192   0.024826   2.586  0.00996 **
Age                       -0.007926   0.005731  -1.383  0.16718   
Gendermale                -0.070614   0.101607  -0.695  0.48735   
Hypertension.compositeyes  0.024561   0.138756   0.177  0.85956   
DiabetesStatusDiabetes    -0.167687   0.115198  -1.456  0.14603   
SmokerCurrentyes           0.006786   0.099727   0.068  0.94577   
Med.Statin.LLDyes         -0.258587   0.116721  -2.215  0.02712 * 
Med.all.antiplateletyes   -0.161527   0.163593  -0.987  0.32387   
GFR_MDRD                  -0.004904   0.002499  -1.962  0.05024 . 
BMI                       -0.017520   0.012739  -1.375  0.16955   
CAD_history               -0.055607   0.105034  -0.529  0.59672   
Stroke_history             0.056941   0.099170   0.574  0.56607   
Peripheral.interv          0.298235   0.120881   2.467  0.01390 * 
stenose50-70%             -0.144222   0.822263  -0.175  0.86083   
stenose70-90%              0.098077   0.795684   0.123  0.90194   
stenose90-99%              0.174778   0.794935   0.220  0.82605   
stenose100% (Occlusion)   -0.277654   0.917836  -0.303  0.76237   
stenose70-99%             -1.386821   1.125420  -1.232  0.21834   
LDL_final                  0.016695   0.046789   0.357  0.72136   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.109 on 584 degrees of freedom
Multiple R-squared:  0.06059,   Adjusted R-squared:  0.03002 
F-statistic: 1.982 on 19 and 584 DF,  p-value: 0.007875

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0.064192 
Standard error............: 0.024826 
Odds ratio (effect size)..: 1.066 
Lower 95% CI..............: 1.016 
Upper 95% CI..............: 1.119 
T-value...................: 2.585637 
P-value...................: 0.009960997 
R^2.......................: 0.060586 
Adjusted r^2..............: 0.030023 
Sample size of AE DB......: 2388 
Sample size of model......: 604 
Missing data %............: 74.70687 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + DiabetesStatus + Med.Statin.LLD + 
    GFR_MDRD + Peripheral.interv, data = currentDF)

Coefficients:
           (Intercept)                     Age  DiabetesStatusDiabetes       Med.Statin.LLDyes                GFR_MDRD  
             -0.645427               -0.010345               -0.159295               -0.283019               -0.004953  
     Peripheral.interv  
              0.307515  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.6508 -0.4774  0.1193  0.6125  2.7379 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -0.571572   1.028428  -0.556   0.5786  
currentDF[, TRAIT]         0.028425   0.032160   0.884   0.3771  
Age                       -0.009415   0.005714  -1.648   0.0999 .
Gendermale                -0.009854   0.101751  -0.097   0.9229  
Hypertension.compositeyes  0.047185   0.136267   0.346   0.7293  
DiabetesStatusDiabetes    -0.159308   0.115018  -1.385   0.1666  
SmokerCurrentyes           0.018075   0.099297   0.182   0.8556  
Med.Statin.LLDyes         -0.265016   0.115598  -2.293   0.0222 *
Med.all.antiplateletyes   -0.144961   0.162054  -0.895   0.3714  
GFR_MDRD                  -0.004824   0.002503  -1.928   0.0544 .
BMI                       -0.010965   0.012533  -0.875   0.3820  
CAD_history               -0.071840   0.105008  -0.684   0.4942  
Stroke_history             0.082184   0.098712   0.833   0.4054  
Peripheral.interv          0.299575   0.121917   2.457   0.0143 *
stenose50-70%             -0.129023   0.821241  -0.157   0.8752  
stenose70-90%              0.126408   0.794746   0.159   0.8737  
stenose90-99%              0.210391   0.793876   0.265   0.7911  
stenose100% (Occlusion)   -0.293635   0.899684  -0.326   0.7443  
stenose70-99%             -1.262022   1.124404  -1.122   0.2622  
LDL_final                  0.023878   0.046958   0.508   0.6113  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.107 on 587 degrees of freedom
Multiple R-squared:  0.05271,   Adjusted R-squared:  0.02205 
F-statistic: 1.719 on 19 and 587 DF,  p-value: 0.02935

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: 0.028425 
Standard error............: 0.03216 
Odds ratio (effect size)..: 1.029 
Lower 95% CI..............: 0.966 
Upper 95% CI..............: 1.096 
T-value...................: 0.88386 
P-value...................: 0.3771337 
R^2.......................: 0.052709 
Adjusted r^2..............: 0.022047 
Sample size of AE DB......: 2388 
Sample size of model......: 607 
Missing data %............: 74.58124 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Med.Statin.LLD + GFR_MDRD + 
    Peripheral.interv, data = currentDF)

Coefficients:
      (Intercept)                Age  Med.Statin.LLDyes           GFR_MDRD  Peripheral.interv  
        -0.623263          -0.010168          -0.321089          -0.005272           0.269837  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.6588 -0.4881  0.1029  0.6320  2.6591 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -0.409939   1.076240  -0.381   0.7034  
currentDF[, TRAIT]         0.051036   0.060112   0.849   0.3963  
Age                       -0.010250   0.006021  -1.702   0.0892 .
Gendermale                -0.023004   0.107103  -0.215   0.8300  
Hypertension.compositeyes -0.013676   0.148086  -0.092   0.9265  
DiabetesStatusDiabetes    -0.139140   0.125480  -1.109   0.2680  
SmokerCurrentyes          -0.031038   0.105613  -0.294   0.7690  
Med.Statin.LLDyes         -0.289430   0.123728  -2.339   0.0197 *
Med.all.antiplateletyes   -0.128154   0.180604  -0.710   0.4783  
GFR_MDRD                  -0.005528   0.002700  -2.048   0.0411 *
BMI                       -0.014719   0.013397  -1.099   0.2724  
CAD_history               -0.068964   0.113307  -0.609   0.5430  
Stroke_history             0.043365   0.105700   0.410   0.6818  
Peripheral.interv          0.268876   0.131623   2.043   0.0416 *
stenose50-70%             -0.129415   0.840865  -0.154   0.8777  
stenose70-90%              0.154123   0.810665   0.190   0.8493  
stenose90-99%              0.225741   0.809735   0.279   0.7805  
stenose100% (Occlusion)   -0.238605   0.935267  -0.255   0.7987  
stenose70-99%             -1.686131   1.410283  -1.196   0.2324  
LDL_final                  0.034234   0.051289   0.667   0.5048  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.128 on 534 degrees of freedom
Multiple R-squared:  0.05257,   Adjusted R-squared:  0.01886 
F-statistic: 1.559 on 19 and 534 DF,  p-value: 0.06155

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: 0.051036 
Standard error............: 0.060112 
Odds ratio (effect size)..: 1.052 
Lower 95% CI..............: 0.935 
Upper 95% CI..............: 1.184 
T-value...................: 0.849019 
P-value...................: 0.396251 
R^2.......................: 0.052566 
Adjusted r^2..............: 0.018856 
Sample size of AE DB......: 2388 
Sample size of model......: 554 
Missing data %............: 76.80067 

Analysis of MCP1_pg_ug_2015_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + Hypertension.composite + 
    Stroke_history + LDL_final, data = currentDF)

Coefficients:
              (Intercept)                 Gendermale  Hypertension.compositeyes             Stroke_history                  LDL_final  
                  -1.5707                     0.1726                    -0.3481                     0.3069                     0.1504  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.0660 -0.7930  0.0067  0.8502  3.5632 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -0.929404   1.105948  -0.840  0.40103   
currentDF[, TRAIT]        -0.006916   0.029359  -0.236  0.81385   
Age                       -0.003860   0.006678  -0.578  0.56345   
Gendermale                 0.177357   0.119206   1.488  0.13732   
Hypertension.compositeyes -0.333981   0.164211  -2.034  0.04240 * 
DiabetesStatusDiabetes    -0.088384   0.134409  -0.658  0.51106   
SmokerCurrentyes          -0.072973   0.116824  -0.625  0.53244   
Med.Statin.LLDyes         -0.183941   0.136585  -1.347  0.17858   
Med.all.antiplateletyes   -0.177579   0.188679  -0.941  0.34700   
GFR_MDRD                  -0.003332   0.002879  -1.157  0.24760   
BMI                       -0.011705   0.014568  -0.803  0.42202   
CAD_history                0.075332   0.122823   0.613  0.53988   
Stroke_history             0.317516   0.116987   2.714  0.00683 **
Peripheral.interv          0.064670   0.141725   0.456  0.64833   
stenose50-70%              0.194101   0.815272   0.238  0.81190   
stenose70-90%              0.607022   0.777643   0.781  0.43535   
stenose90-99%              0.447575   0.776967   0.576  0.56479   
stenose100% (Occlusion)   -0.665847   0.952000  -0.699  0.48456   
stenose70-99%              0.775391   1.231567   0.630  0.52920   
LDL_final                  0.135784   0.054445   2.494  0.01290 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.328 on 606 degrees of freedom
Multiple R-squared:  0.05948,   Adjusted R-squared:  0.02999 
F-statistic: 2.017 on 19 and 606 DF,  p-value: 0.006522

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: -0.006916 
Standard error............: 0.029359 
Odds ratio (effect size)..: 0.993 
Lower 95% CI..............: 0.938 
Upper 95% CI..............: 1.052 
T-value...................: -0.235562 
P-value...................: 0.8138524 
R^2.......................: 0.059478 
Adjusted r^2..............: 0.029989 
Sample size of AE DB......: 2388 
Sample size of model......: 626 
Missing data %............: 73.7856 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    Stroke_history + LDL_final, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes             Stroke_history                  LDL_final  
                  -1.4877                    -0.1145                    -0.3259                     0.2511                     0.1689  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8298 -0.7851 -0.0041  0.8556  3.5190 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -0.959118   1.098926  -0.873  0.38313   
currentDF[, TRAIT]        -0.105998   0.037524  -2.825  0.00489 **
Age                       -0.005301   0.006643  -0.798  0.42518   
Gendermale                 0.146474   0.119488   1.226  0.22073   
Hypertension.compositeyes -0.310950   0.161119  -1.930  0.05408 . 
DiabetesStatusDiabetes    -0.126107   0.134058  -0.941  0.34724   
SmokerCurrentyes          -0.073389   0.116179  -0.632  0.52783   
Med.Statin.LLDyes         -0.180246   0.135211  -1.333  0.18301   
Med.all.antiplateletyes   -0.197609   0.186793  -1.058  0.29052   
GFR_MDRD                  -0.002675   0.002879  -0.929  0.35322   
BMI                       -0.010517   0.014344  -0.733  0.46372   
CAD_history                0.067123   0.122608   0.547  0.58426   
Stroke_history             0.276835   0.116332   2.380  0.01763 * 
Peripheral.interv          0.115307   0.142703   0.808  0.41940   
stenose50-70%              0.224709   0.812884   0.276  0.78231   
stenose70-90%              0.642432   0.775621   0.828  0.40784   
stenose90-99%              0.513547   0.774793   0.663  0.50770   
stenose100% (Occlusion)   -0.304064   0.924098  -0.329  0.74224   
stenose70-99%              0.907983   1.228759   0.739  0.46023   
LDL_final                  0.154355   0.054558   2.829  0.00482 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.324 on 609 degrees of freedom
Multiple R-squared:  0.07024,   Adjusted R-squared:  0.04124 
F-statistic: 2.422 on 19 and 609 DF,  p-value: 0.0006923

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.105998 
Standard error............: 0.037524 
Odds ratio (effect size)..: 0.899 
Lower 95% CI..............: 0.836 
Upper 95% CI..............: 0.968 
T-value...................: -2.824808 
P-value...................: 0.004885846 
R^2.......................: 0.070244 
Adjusted r^2..............: 0.041237 
Sample size of AE DB......: 2388 
Sample size of model......: 629 
Missing data %............: 73.65997 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    Hypertension.composite + Stroke_history + LDL_final, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                 Gendermale  Hypertension.compositeyes             Stroke_history  
                  -1.5191                    -0.1567                     0.2528                    -0.2838                     0.3058  
                LDL_final  
                   0.1790  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.8851 -0.7703  0.0065  0.8631  3.5802 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -0.523924   1.144057  -0.458   0.6472   
currentDF[, TRAIT]        -0.150564   0.068731  -2.191   0.0289 * 
Age                       -0.004199   0.006947  -0.604   0.5458   
Gendermale                 0.247989   0.124551   1.991   0.0470 * 
Hypertension.compositeyes -0.260669   0.174608  -1.493   0.1360   
DiabetesStatusDiabetes    -0.080503   0.144545  -0.557   0.5778   
SmokerCurrentyes          -0.038813   0.122667  -0.316   0.7518   
Med.Statin.LLDyes         -0.172549   0.143615  -1.201   0.2301   
Med.all.antiplateletyes   -0.151041   0.205188  -0.736   0.4620   
GFR_MDRD                  -0.003511   0.003061  -1.147   0.2519   
BMI                       -0.018790   0.015167  -1.239   0.2159   
CAD_history                0.081761   0.131532   0.622   0.5345   
Stroke_history             0.312108   0.123405   2.529   0.0117 * 
Peripheral.interv          0.010985   0.152518   0.072   0.9426   
stenose50-70%             -0.056253   0.827227  -0.068   0.9458   
stenose70-90%              0.437868   0.785986   0.557   0.5777   
stenose90-99%              0.309260   0.785037   0.394   0.6938   
stenose100% (Occlusion)   -0.769104   0.961372  -0.800   0.4240   
stenose70-99%              0.904530   1.579083   0.573   0.5670   
LDL_final                  0.159352   0.058973   2.702   0.0071 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.339 on 555 degrees of freedom
Multiple R-squared:  0.07268,   Adjusted R-squared:  0.04094 
F-statistic:  2.29 on 19 and 555 DF,  p-value: 0.001509

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.150564 
Standard error............: 0.068731 
Odds ratio (effect size)..: 0.86 
Lower 95% CI..............: 0.752 
Upper 95% CI..............: 0.984 
T-value...................: -2.190616 
P-value...................: 0.0288948 
R^2.......................: 0.072685 
Adjusted r^2..............: 0.040939 
Sample size of AE DB......: 2388 
Sample size of model......: 575 
Missing data %............: 75.92127 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes  
                  -0.4964                     0.8687  

Degrees of Freedom: 286 Total (i.e. Null);  285 Residual
Null Deviance:      393.1 
Residual Deviance: 387.1    AIC: 391.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8203  -1.2203   0.7912   1.0267   1.6584  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -14.286196 882.746445  -0.016   0.9871  
currentDF[, PROTEIN]        0.127711   0.117619   1.086   0.2776  
Age                        -0.007405   0.017177  -0.431   0.6664  
Gendermale                 -0.107807   0.294894  -0.366   0.7147  
Hypertension.compositeyes   0.943344   0.388474   2.428   0.0152 *
DiabetesStatusDiabetes     -0.282098   0.340209  -0.829   0.4070  
SmokerCurrentyes           -0.158885   0.271685  -0.585   0.5587  
Med.Statin.LLDyes          -0.387047   0.306268  -1.264   0.2063  
Med.all.antiplateletyes     0.693695   0.541645   1.281   0.2003  
GFR_MDRD                   -0.009341   0.007815  -1.195   0.2320  
BMI                        -0.009798   0.034417  -0.285   0.7759  
CAD_history                 0.267935   0.287363   0.932   0.3511  
Stroke_history             -0.132062   0.271241  -0.487   0.6263  
Peripheral.interv          -0.396651   0.313421  -1.266   0.2057  
stenose50-70%              15.622260 882.743866   0.018   0.9859  
stenose70-90%              15.078260 882.743546   0.017   0.9864  
stenose90-99%              14.713152 882.743540   0.017   0.9867  
stenose100% (Occlusion)    16.300162 882.744474   0.018   0.9853  
LDL_final                  -0.134622   0.134950  -0.998   0.3185  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 393.08  on 286  degrees of freedom
Residual deviance: 372.59  on 268  degrees of freedom
AIC: 410.59

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.127711 
Standard error............: 0.117619 
Odds ratio (effect size)..: 1.136 
Lower 95% CI..............: 0.902 
Upper 95% CI..............: 1.431 
Z-value...................: 1.085796 
P-value...................: 0.2775691 
Hosmer and Lemeshow r^2...: 0.052141 
Cox and Snell r^2.........: 0.068924 
Nagelkerke's pseudo r^2...: 0.092416 
Sample size of AE DB......: 2388 
Sample size of model......: 287 
Missing data %............: 87.98158 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Med.all.antiplatelet + 
    GFR_MDRD + stenose, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)  Med.all.antiplateletyes                 GFR_MDRD            stenose50-70%            stenose70-90%  
               17.50951                  0.82920                 -0.01174                  0.26966                -16.02111  
          stenose90-99%  stenose100% (Occlusion)  
              -16.41110                  0.85461  

Degrees of Freedom: 285 Total (i.e. Null);  279 Residual
Null Deviance:      304.1 
Residual Deviance: 291.8    AIC: 305.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.00491   0.00023   0.63115   0.76535   1.18759  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)
(Intercept)                1.755e+01  3.956e+03   0.004    0.996
currentDF[, PROTEIN]       6.100e-02  1.397e-01   0.437    0.662
Age                        8.498e-03  2.038e-02   0.417    0.677
Gendermale                -2.791e-01  3.615e-01  -0.772    0.440
Hypertension.compositeyes  4.528e-01  4.345e-01   1.042    0.297
DiabetesStatusDiabetes     4.050e-01  4.292e-01   0.944    0.345
SmokerCurrentyes           3.410e-01  3.259e-01   1.046    0.295
Med.Statin.LLDyes         -2.690e-01  3.648e-01  -0.737    0.461
Med.all.antiplateletyes    8.442e-01  5.714e-01   1.477    0.140
GFR_MDRD                  -1.028e-02  9.278e-03  -1.108    0.268
BMI                       -3.652e-02  4.094e-02  -0.892    0.372
CAD_history               -1.245e-02  3.377e-01  -0.037    0.971
Stroke_history             2.234e-01  3.336e-01   0.670    0.503
Peripheral.interv         -2.317e-01  3.598e-01  -0.644    0.519
stenose50-70%              1.679e-01  4.163e+03   0.000    1.000
stenose70-90%             -1.614e+01  3.956e+03  -0.004    0.997
stenose90-99%             -1.661e+01  3.956e+03  -0.004    0.997
stenose100% (Occlusion)    6.788e-01  4.417e+03   0.000    1.000
LDL_final                 -1.338e-02  1.608e-01  -0.083    0.934

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 304.10  on 285  degrees of freedom
Residual deviance: 285.69  on 267  degrees of freedom
AIC: 323.69

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.060996 
Standard error............: 0.139671 
Odds ratio (effect size)..: 1.063 
Lower 95% CI..............: 0.808 
Upper 95% CI..............: 1.398 
Z-value...................: 0.43671 
P-value...................: 0.6623216 
Hosmer and Lemeshow r^2...: 0.060544 
Cox and Snell r^2.........: 0.062348 
Nagelkerke's pseudo r^2...: 0.095233 
Sample size of AE DB......: 2388 
Sample size of model......: 286 
Missing data %............: 88.02345 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)                 Gendermale  Hypertension.compositeyes  
                   0.1798                     0.6324                     1.0185  

Degrees of Freedom: 286 Total (i.e. Null);  284 Residual
Null Deviance:      277.5 
Residual Deviance: 267.3    AIC: 273.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4640   0.3869   0.5163   0.6631   1.2321  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.331e+01  2.400e+03   0.006   0.9956   
currentDF[, PROTEIN]       5.848e-02  1.460e-01   0.401   0.6887   
Age                       -6.607e-03  2.259e-02  -0.292   0.7699   
Gendermale                 8.909e-01  3.638e-01   2.449   0.0143 * 
Hypertension.compositeyes  1.129e+00  4.236e-01   2.665   0.0077 **
DiabetesStatusDiabetes    -1.347e-01  4.364e-01  -0.309   0.7577   
SmokerCurrentyes           3.119e-01  3.609e-01   0.864   0.3875   
Med.Statin.LLDyes          7.371e-02  3.929e-01   0.188   0.8512   
Med.all.antiplateletyes    6.757e-01  6.391e-01   1.057   0.2904   
GFR_MDRD                  -8.549e-03  1.040e-02  -0.822   0.4111   
BMI                        3.147e-02  4.539e-02   0.693   0.4881   
CAD_history               -2.786e-02  3.702e-01  -0.075   0.9400   
Stroke_history             2.389e-01  3.632e-01   0.658   0.5107   
Peripheral.interv         -3.988e-01  3.797e-01  -1.050   0.2937   
stenose50-70%             -1.608e+01  2.400e+03  -0.007   0.9947   
stenose70-90%             -1.460e+01  2.400e+03  -0.006   0.9951   
stenose90-99%             -1.495e+01  2.400e+03  -0.006   0.9950   
stenose100% (Occlusion)    9.700e-01  2.666e+03   0.000   0.9997   
LDL_final                  2.326e-01  1.886e-01   1.233   0.2176   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 277.55  on 286  degrees of freedom
Residual deviance: 254.25  on 268  degrees of freedom
AIC: 292.25

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.058484 
Standard error............: 0.146006 
Odds ratio (effect size)..: 1.06 
Lower 95% CI..............: 0.796 
Upper 95% CI..............: 1.412 
Z-value...................: 0.400559 
P-value...................: 0.6887448 
Hosmer and Lemeshow r^2...: 0.083927 
Cox and Snell r^2.........: 0.077957 
Nagelkerke's pseudo r^2...: 0.125777 
Sample size of AE DB......: 2388 
Sample size of model......: 287 
Missing data %............: 87.98158 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Hypertension.composite + 
    DiabetesStatus, family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)                        Age  Hypertension.compositeyes     DiabetesStatusDiabetes  
                 -1.60096                    0.03481                    0.61722                   -0.58861  

Degrees of Freedom: 286 Total (i.e. Null);  283 Residual
Null Deviance:      323.3 
Residual Deviance: 313.4    AIC: 321.4

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9750  -0.5510   0.6402   0.7877   1.2400  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                11.218363 882.747304   0.013   0.9899  
currentDF[, PROTEIN]       -0.088057   0.127517  -0.691   0.4898  
Age                         0.034226   0.019081   1.794   0.0729 .
Gendermale                  0.371138   0.322752   1.150   0.2502  
Hypertension.compositeyes   0.644421   0.400128   1.611   0.1073  
DiabetesStatusDiabetes     -0.578214   0.362096  -1.597   0.1103  
SmokerCurrentyes            0.176265   0.309514   0.569   0.5690  
Med.Statin.LLDyes           0.045481   0.344011   0.132   0.8948  
Med.all.antiplateletyes    -0.109971   0.637921  -0.172   0.8631  
GFR_MDRD                   -0.002540   0.008826  -0.288   0.7735  
BMI                         0.007721   0.038101   0.203   0.8394  
CAD_history                 0.066670   0.328960   0.203   0.8394  
Stroke_history              0.118372   0.311575   0.380   0.7040  
Peripheral.interv           0.143803   0.361519   0.398   0.6908  
stenose50-70%             -12.563912 882.744016  -0.014   0.9886  
stenose70-90%             -12.969217 882.743594  -0.015   0.9883  
stenose90-99%             -13.023753 882.743587  -0.015   0.9882  
stenose100% (Occlusion)   -12.810939 882.744556  -0.015   0.9884  
LDL_final                   0.054494   0.155443   0.351   0.7259  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 323.33  on 286  degrees of freedom
Residual deviance: 310.07  on 268  degrees of freedom
AIC: 348.07

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: IPH 
Effect size...............: -0.088057 
Standard error............: 0.127517 
Odds ratio (effect size)..: 0.916 
Lower 95% CI..............: 0.713 
Upper 95% CI..............: 1.176 
Z-value...................: -0.69055 
P-value...................: 0.4898483 
Hosmer and Lemeshow r^2...: 0.041014 
Cox and Snell r^2.........: 0.045154 
Nagelkerke's pseudo r^2...: 0.06681 
Sample size of AE DB......: 2388 
Sample size of model......: 287 
Missing data %............: 87.98158 

Analysis of MCP1_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    DiabetesStatus + Stroke_history + Peripheral.interv + LDL_final, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes     DiabetesStatusDiabetes             Stroke_history          Peripheral.interv  
                   0.6107                     0.6170                    -0.5058                    -0.3380                    -0.5331  
                LDL_final  
                  -0.1784  

Degrees of Freedom: 356 Total (i.e. Null);  351 Residual
Null Deviance:      487 
Residual Deviance: 473  AIC: 485

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9077  -1.1891   0.8111   1.0314   1.6747  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -13.508653 535.415008  -0.025   0.9799  
currentDF[, PROTEIN]       -0.117467   0.133102  -0.883   0.3775  
Age                         0.011647   0.014663   0.794   0.4270  
Gendermale                  0.050964   0.261515   0.195   0.8455  
Hypertension.compositeyes   0.633766   0.327723   1.934   0.0531 .
DiabetesStatusDiabetes     -0.551528   0.299781  -1.840   0.0658 .
SmokerCurrentyes            0.228471   0.242198   0.943   0.3455  
Med.Statin.LLDyes          -0.370960   0.278127  -1.334   0.1823  
Med.all.antiplateletyes     0.476326   0.480376   0.992   0.3214  
GFR_MDRD                   -0.004611   0.006700  -0.688   0.4914  
BMI                         0.017891   0.029667   0.603   0.5465  
CAD_history                 0.054534   0.260587   0.209   0.8342  
Stroke_history             -0.372281   0.242884  -1.533   0.1253  
Peripheral.interv          -0.502538   0.290451  -1.730   0.0836 .
stenose50-70%              14.016330 535.411739   0.026   0.9791  
stenose70-90%              13.744409 535.411344   0.026   0.9795  
stenose90-99%              13.371687 535.411338   0.025   0.9801  
stenose100% (Occlusion)    14.603558 535.412918   0.027   0.9782  
LDL_final                  -0.215662   0.117549  -1.835   0.0666 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 487.01  on 356  degrees of freedom
Residual deviance: 463.02  on 338  degrees of freedom
AIC: 501.02

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.117467 
Standard error............: 0.133102 
Odds ratio (effect size)..: 0.889 
Lower 95% CI..............: 0.685 
Upper 95% CI..............: 1.154 
Z-value...................: -0.88253 
P-value...................: 0.3774901 
Hosmer and Lemeshow r^2...: 0.049252 
Cox and Snell r^2.........: 0.064981 
Nagelkerke's pseudo r^2...: 0.087292 
Sample size of AE DB......: 2388 
Sample size of model......: 357 
Missing data %............: 85.05025 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]            stenose50-70%            stenose70-90%            stenose90-99%  
                21.5615                  -0.7445                  -0.3016                 -16.3553                 -16.8403  
stenose100% (Occlusion)  
                -1.0187  

Degrees of Freedom: 355 Total (i.e. Null);  350 Residual
Null Deviance:      361.2 
Residual Deviance: 331.1    AIC: 343.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5050   0.0963   0.5487   0.7158   1.3987  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                2.073e+01  3.956e+03   0.005    0.996    
currentDF[, PROTEIN]      -7.579e-01  1.895e-01  -4.000 6.33e-05 ***
Age                        8.898e-03  1.858e-02   0.479    0.632    
Gendermale                -1.202e-01  3.466e-01  -0.347    0.729    
Hypertension.compositeyes  3.705e-01  3.935e-01   0.942    0.346    
DiabetesStatusDiabetes     3.649e-01  4.067e-01   0.897    0.370    
SmokerCurrentyes           3.815e-01  3.075e-01   1.241    0.215    
Med.Statin.LLDyes         -7.689e-02  3.434e-01  -0.224    0.823    
Med.all.antiplateletyes    8.168e-01  5.622e-01   1.453    0.146    
GFR_MDRD                  -5.648e-03  8.561e-03  -0.660    0.509    
BMI                       -7.924e-03  3.978e-02  -0.199    0.842    
CAD_history                5.255e-04  3.226e-01   0.002    0.999    
Stroke_history             2.142e-01  3.126e-01   0.685    0.493    
Peripheral.interv         -3.928e-01  3.537e-01  -1.110    0.267    
stenose50-70%             -4.636e-01  4.122e+03   0.000    1.000    
stenose70-90%             -1.654e+01  3.956e+03  -0.004    0.997    
stenose90-99%             -1.714e+01  3.956e+03  -0.004    0.997    
stenose100% (Occlusion)   -4.924e-01  4.323e+03   0.000    1.000    
LDL_final                  1.341e-02  1.490e-01   0.090    0.928    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 361.22  on 355  degrees of freedom
Residual deviance: 322.97  on 337  degrees of freedom
AIC: 360.97

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.757933 
Standard error............: 0.189472 
Odds ratio (effect size)..: 0.469 
Lower 95% CI..............: 0.323 
Upper 95% CI..............: 0.679 
Z-value...................: -4.00023 
P-value...................: 6.32809e-05 
Hosmer and Lemeshow r^2...: 0.105893 
Cox and Snell r^2.........: 0.101875 
Nagelkerke's pseudo r^2...: 0.15981 
Sample size of AE DB......: 2388 
Sample size of model......: 356 
Missing data %............: 85.09213 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Hypertension.composite + Stroke_history + LDL_final, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]                 Gendermale  Hypertension.compositeyes             Stroke_history  
                  -3.7831                     0.6416                     0.8985                     1.0321                     0.5088  
                LDL_final  
                   0.2259  

Degrees of Freedom: 356 Total (i.e. Null);  351 Residual
Null Deviance:      356.2 
Residual Deviance: 319.9    AIC: 331.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4238   0.2761   0.4627   0.6594   1.7797  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 6.507205 882.747666   0.007  0.99412    
currentDF[, PROTEIN]        0.697802   0.177591   3.929 8.52e-05 ***
Age                         0.001098   0.018968   0.058  0.95384    
Gendermale                  1.003262   0.317905   3.156  0.00160 ** 
Hypertension.compositeyes   1.044345   0.398278   2.622  0.00874 ** 
DiabetesStatusDiabetes     -0.213865   0.380839  -0.562  0.57442    
SmokerCurrentyes            0.437856   0.318979   1.373  0.16985    
Med.Statin.LLDyes           0.039751   0.364668   0.109  0.91320    
Med.all.antiplateletyes     0.760893   0.562010   1.354  0.17578    
GFR_MDRD                   -0.004178   0.009046  -0.462  0.64418    
BMI                         0.029718   0.038285   0.776  0.43761    
CAD_history                -0.111178   0.340924  -0.326  0.74434    
Stroke_history              0.403651   0.333402   1.211  0.22601    
Peripheral.interv          -0.049242   0.363705  -0.135  0.89230    
stenose50-70%             -13.468540 882.743867  -0.015  0.98783    
stenose70-90%             -11.588378 882.743592  -0.013  0.98953    
stenose90-99%             -12.172795 882.743575  -0.014  0.98900    
stenose100% (Occlusion)   -11.208317 882.744476  -0.013  0.98987    
LDL_final                   0.243454   0.161401   1.508  0.13146    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 356.18  on 356  degrees of freedom
Residual deviance: 307.64  on 338  degrees of freedom
AIC: 345.64

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.697802 
Standard error............: 0.177591 
Odds ratio (effect size)..: 2.009 
Lower 95% CI..............: 1.419 
Upper 95% CI..............: 2.846 
Z-value...................: 3.92926 
P-value...................: 8.520779e-05 
Hosmer and Lemeshow r^2...: 0.136261 
Cox and Snell r^2.........: 0.127111 
Nagelkerke's pseudo r^2...: 0.201357 
Sample size of AE DB......: 2388 
Sample size of model......: 357 
Missing data %............: 85.05025 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    DiabetesStatus, family = binomial(link = "logit"), data = currentDF)

Coefficients:
           (Intercept)                     Age              Gendermale  DiabetesStatusDiabetes  
              -0.96814                 0.02645                 0.56473                -0.49874  

Degrees of Freedom: 356 Total (i.e. Null);  353 Residual
Null Deviance:      405.3 
Residual Deviance: 393.4    AIC: 401.4

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0551  -1.0934   0.6553   0.7927   1.3321  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                10.465152 882.746422   0.012   0.9905  
currentDF[, PROTEIN]       -0.001767   0.151910  -0.012   0.9907  
Age                         0.023315   0.016565   1.407   0.1593  
Gendermale                  0.647257   0.282325   2.293   0.0219 *
Hypertension.compositeyes   0.264155   0.361345   0.731   0.4648  
DiabetesStatusDiabetes     -0.513751   0.318782  -1.612   0.1070  
SmokerCurrentyes            0.043114   0.272151   0.158   0.8741  
Med.Statin.LLDyes          -0.228883   0.321223  -0.713   0.4761  
Med.all.antiplateletyes     0.604463   0.505534   1.196   0.2318  
GFR_MDRD                   -0.003312   0.007689  -0.431   0.6667  
BMI                         0.020180   0.032770   0.616   0.5380  
CAD_history                 0.209279   0.302856   0.691   0.4896  
Stroke_history              0.190935   0.278732   0.685   0.4933  
Peripheral.interv           0.425060   0.347900   1.222   0.2218  
stenose50-70%             -12.083404 882.743894  -0.014   0.9891  
stenose70-90%             -12.665178 882.743525  -0.014   0.9886  
stenose90-99%             -12.604666 882.743521  -0.014   0.9886  
stenose100% (Occlusion)   -12.113730 882.744448  -0.014   0.9891  
LDL_final                   0.067077   0.135123   0.496   0.6196  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 405.31  on 356  degrees of freedom
Residual deviance: 386.17  on 338  degrees of freedom
AIC: 424.17

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: IPH 
Effect size...............: -0.001767 
Standard error............: 0.15191 
Odds ratio (effect size)..: 0.998 
Lower 95% CI..............: 0.741 
Upper 95% CI..............: 1.344 
Z-value...................: -0.011635 
P-value...................: 0.9907169 
Hosmer and Lemeshow r^2...: 0.047226 
Cox and Snell r^2.........: 0.052204 
Nagelkerke's pseudo r^2...: 0.07692 
Sample size of AE DB......: 2388 
Sample size of model......: 357 
Missing data %............: 85.05025 

Analysis of IL6_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent + 
    LDL_final, family = binomial(link = "logit"), data = currentDF)

Coefficients:
     (Intercept)               Age  SmokerCurrentyes         LDL_final  
        -1.27196           0.02486           0.41455          -0.16393  

Degrees of Freedom: 619 Total (i.e. Null);  616 Residual
Null Deviance:      857.9 
Residual Deviance: 843  AIC: 851

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7301  -1.1762   0.8182   1.0943   1.7609  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                -1.830909   1.736196  -1.055  0.29163   
currentDF[, PROTEIN]       -0.028728   0.057440  -0.500  0.61698   
Age                         0.029085   0.010297   2.825  0.00474 **
Gendermale                  0.053215   0.184708   0.288  0.77327   
Hypertension.compositeyes   0.190353   0.256764   0.741  0.45848   
DiabetesStatusDiabetes     -0.117627   0.208990  -0.563  0.57355   
SmokerCurrentyes            0.474415   0.183594   2.584  0.00976 **
Med.Statin.LLDyes          -0.273636   0.211301  -1.295  0.19532   
Med.all.antiplateletyes     0.133791   0.293567   0.456  0.64858   
GFR_MDRD                    0.005052   0.004528   1.116  0.26449   
BMI                         0.018513   0.022445   0.825  0.40948   
CAD_history                 0.017676   0.193243   0.091  0.92712   
Stroke_history             -0.224433   0.181531  -1.236  0.21633   
Peripheral.interv          -0.330490   0.219559  -1.505  0.13226   
stenose50-70%              -1.298081   1.303665  -0.996  0.31939   
stenose70-90%              -0.541387   1.245175  -0.435  0.66372   
stenose90-99%              -0.445735   1.243566  -0.358  0.72002   
stenose100% (Occlusion)     0.165121   1.512199   0.109  0.91305   
stenose70-99%             -15.714990 605.326073  -0.026  0.97929   
LDL_final                  -0.209000   0.086579  -2.414  0.01578 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 857.85  on 619  degrees of freedom
Residual deviance: 824.82  on 600  degrees of freedom
AIC: 864.82

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.028728 
Standard error............: 0.05744 
Odds ratio (effect size)..: 0.972 
Lower 95% CI..............: 0.868 
Upper 95% CI..............: 1.087 
Z-value...................: -0.500136 
P-value...................: 0.6169792 
Hosmer and Lemeshow r^2...: 0.038502 
Cox and Snell r^2.........: 0.051879 
Nagelkerke's pseudo r^2...: 0.069234 
Sample size of AE DB......: 2388 
Sample size of model......: 620 
Missing data %............: 74.03685 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerCurrent, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]      SmokerCurrentyes  
              0.5975               -0.1769                0.5134  

Degrees of Freedom: 621 Total (i.e. Null);  619 Residual
Null Deviance:      643 
Residual Deviance: 631.3    AIC: 637.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2201   0.3945   0.6217   0.7404   1.0463  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.347e+01  1.384e+03   0.010   0.9922  
currentDF[, PROTEIN]      -1.592e-01  7.048e-02  -2.259   0.0239 *
Age                        3.108e-03  1.246e-02   0.249   0.8030  
Gendermale                -9.898e-02  2.255e-01  -0.439   0.6608  
Hypertension.compositeyes  7.679e-02  3.053e-01   0.251   0.8014  
DiabetesStatusDiabetes     2.206e-01  2.605e-01   0.847   0.3971  
SmokerCurrentyes           5.213e-01  2.291e-01   2.276   0.0229 *
Med.Statin.LLDyes          1.039e-01  2.526e-01   0.411   0.6809  
Med.all.antiplateletyes    6.244e-02  3.510e-01   0.178   0.8588  
GFR_MDRD                   5.358e-03  5.437e-03   0.986   0.3244  
BMI                        3.671e-02  2.916e-02   1.259   0.2080  
CAD_history                2.909e-01  2.390e-01   1.217   0.2235  
Stroke_history             1.574e-01  2.219e-01   0.709   0.4782  
Peripheral.interv          1.187e-01  2.761e-01   0.430   0.6672  
stenose50-70%             -1.425e+01  1.384e+03  -0.010   0.9918  
stenose70-90%             -1.512e+01  1.384e+03  -0.011   0.9913  
stenose90-99%             -1.511e+01  1.384e+03  -0.011   0.9913  
stenose100% (Occlusion)    4.083e-01  1.646e+03   0.000   0.9998  
stenose70-99%              3.207e-01  2.189e+03   0.000   0.9999  
LDL_final                  1.251e-01  1.046e-01   1.196   0.2316  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 643.00  on 621  degrees of freedom
Residual deviance: 616.63  on 602  degrees of freedom
AIC: 656.63

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.159235 
Standard error............: 0.070477 
Odds ratio (effect size)..: 0.853 
Lower 95% CI..............: 0.743 
Upper 95% CI..............: 0.979 
Z-value...................: -2.259392 
P-value...................: 0.023859 
Hosmer and Lemeshow r^2...: 0.041007 
Cox and Snell r^2.........: 0.041506 
Nagelkerke's pseudo r^2...: 0.064417 
Sample size of AE DB......: 2388 
Sample size of model......: 622 
Missing data %............: 73.9531 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv + LDL_final, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale        Stroke_history     Peripheral.interv             LDL_final  
              1.0123                0.3281                0.9595                0.3484               -0.5311                0.1690  

Degrees of Freedom: 621 Total (i.e. Null);  616 Residual
Null Deviance:      717.6 
Residual Deviance: 658.9    AIC: 670.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3891  -0.9341   0.5910   0.7839   1.4787  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                13.890440 499.839451   0.028   0.9778    
currentDF[, PROTEIN]        0.328587   0.070060   4.690 2.73e-06 ***
Age                         0.004246   0.011788   0.360   0.7187    
Gendermale                  0.984317   0.208140   4.729 2.26e-06 ***
Hypertension.compositeyes   0.166914   0.297912   0.560   0.5753    
DiabetesStatusDiabetes     -0.110118   0.239927  -0.459   0.6463    
SmokerCurrentyes            0.145975   0.213639   0.683   0.4944    
Med.Statin.LLDyes          -0.280475   0.256255  -1.095   0.2737    
Med.all.antiplateletyes     0.217329   0.337911   0.643   0.5201    
GFR_MDRD                   -0.004063   0.005325  -0.763   0.4455    
BMI                         0.014926   0.025508   0.585   0.5585    
CAD_history                 0.160808   0.227127   0.708   0.4789    
Stroke_history              0.350025   0.221650   1.579   0.1143    
Peripheral.interv          -0.583853   0.239819  -2.435   0.0149 *  
stenose50-70%             -13.733097 499.837635  -0.027   0.9781    
stenose70-90%             -13.369672 499.837472  -0.027   0.9787    
stenose90-99%             -13.410025 499.837467  -0.027   0.9786    
stenose100% (Occlusion)   -14.275461 499.838084  -0.029   0.9772    
stenose70-99%             -14.439735 499.839587  -0.029   0.9770    
LDL_final                   0.150654   0.101254   1.488   0.1368    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.61  on 621  degrees of freedom
Residual deviance: 650.20  on 602  degrees of freedom
AIC: 690.2

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.328587 
Standard error............: 0.07006 
Odds ratio (effect size)..: 1.389 
Lower 95% CI..............: 1.211 
Upper 95% CI..............: 1.593 
Z-value...................: 4.690096 
P-value...................: 2.730764e-06 
Hosmer and Lemeshow r^2...: 0.093938 
Cox and Snell r^2.........: 0.102711 
Nagelkerke's pseudo r^2...: 0.150045 
Sample size of AE DB......: 2388 
Sample size of model......: 622 
Missing data %............: 73.9531 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + CAD_history + 
    Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)                      Age               Gendermale         SmokerCurrentyes        Med.Statin.LLDyes  
               -1.75978                  0.01881                  0.57921                  0.34166                 -0.34769  
Med.all.antiplateletyes              CAD_history           Stroke_history        Peripheral.interv  
                0.61066                  0.29144                  0.30926                  0.34737  

Degrees of Freedom: 620 Total (i.e. Null);  612 Residual
Null Deviance:      814.3 
Residual Deviance: 786.5    AIC: 804.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0239  -1.2546   0.7741   0.9506   1.3869  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)   
(Intercept)               -2.316877   1.768092  -1.310   0.1901   
currentDF[, PROTEIN]       0.052884   0.059583   0.888   0.3748   
Age                        0.019546   0.010575   1.848   0.0646 . 
Gendermale                 0.616521   0.188836   3.265   0.0011 **
Hypertension.compositeyes  0.100958   0.262302   0.385   0.7003   
DiabetesStatusDiabetes    -0.109138   0.214949  -0.508   0.6116   
SmokerCurrentyes           0.333127   0.190690   1.747   0.0806 . 
Med.Statin.LLDyes         -0.276458   0.223380  -1.238   0.2159   
Med.all.antiplateletyes    0.603370   0.296094   2.038   0.0416 * 
GFR_MDRD                  -0.001990   0.004712  -0.422   0.6727   
BMI                        0.013982   0.023105   0.605   0.5451   
CAD_history                0.291956   0.203434   1.435   0.1512   
Stroke_history             0.314760   0.192264   1.637   0.1016   
Peripheral.interv          0.334848   0.234754   1.426   0.1538   
stenose50-70%             -0.143551   1.303399  -0.110   0.9123   
stenose70-90%              0.054200   1.249010   0.043   0.9654   
stenose90-99%              0.162808   1.247403   0.131   0.8962   
stenose100% (Occlusion)   -0.001599   1.473861  -0.001   0.9991   
stenose70-99%             -0.827068   1.907263  -0.434   0.6645   
LDL_final                  0.078110   0.088317   0.884   0.3765   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 814.31  on 620  degrees of freedom
Residual deviance: 782.48  on 601  degrees of freedom
AIC: 822.48

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: 0.052884 
Standard error............: 0.059583 
Odds ratio (effect size)..: 1.054 
Lower 95% CI..............: 0.938 
Upper 95% CI..............: 1.185 
Z-value...................: 0.88757 
P-value...................: 0.3747719 
Hosmer and Lemeshow r^2...: 0.039092 
Cox and Snell r^2.........: 0.049969 
Nagelkerke's pseudo r^2...: 0.068401 
Sample size of AE DB......: 2388 
Sample size of model......: 621 
Missing data %............: 73.99497 

Analysis of IL6R_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent + 
    Peripheral.interv + stenose + LDL_final, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)                      Age         SmokerCurrentyes        Peripheral.interv            stenose50-70%  
                13.3099                   0.0214                   0.3256                  -0.3539                 -15.0020  
          stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose70-99%                LDL_final  
               -14.3302                 -14.2143                 -13.5827                 -29.4019                  -0.1460  

Degrees of Freedom: 613 Total (i.e. Null);  604 Residual
Null Deviance:      849.3 
Residual Deviance: 826.6    AIC: 846.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6719  -1.1921   0.8332   1.1105   1.6235  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.255e+01  6.239e+02   0.020   0.9840  
currentDF[, PROTEIN]      -1.070e-01  7.800e-02  -1.371   0.1703  
Age                        2.317e-02  1.049e-02   2.209   0.0272 *
Gendermale                 6.011e-03  1.850e-01   0.032   0.9741  
Hypertension.compositeyes  2.254e-01  2.520e-01   0.895   0.3710  
DiabetesStatusDiabetes    -1.754e-01  2.122e-01  -0.826   0.4086  
SmokerCurrentyes           3.559e-01  1.834e-01   1.940   0.0524 .
Med.Statin.LLDyes         -2.060e-01  2.141e-01  -0.962   0.3359  
Med.all.antiplateletyes    1.227e-01  2.978e-01   0.412   0.6802  
GFR_MDRD                   3.300e-03  4.655e-03   0.709   0.4784  
BMI                        8.552e-03  2.310e-02   0.370   0.7112  
CAD_history                8.926e-04  1.935e-01   0.005   0.9963  
Stroke_history            -1.823e-01  1.817e-01  -1.003   0.3158  
Peripheral.interv         -3.495e-01  2.219e-01  -1.575   0.1153  
stenose50-70%             -1.507e+01  6.239e+02  -0.024   0.9807  
stenose70-90%             -1.432e+01  6.239e+02  -0.023   0.9817  
stenose90-99%             -1.421e+01  6.239e+02  -0.023   0.9818  
stenose100% (Occlusion)   -1.366e+01  6.239e+02  -0.022   0.9825  
stenose70-99%             -2.955e+01  8.717e+02  -0.034   0.9730  
LDL_final                 -1.777e-01  8.767e-02  -2.027   0.0426 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 849.30  on 613  degrees of freedom
Residual deviance: 820.09  on 594  degrees of freedom
AIC: 860.09

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.106974 
Standard error............: 0.078004 
Odds ratio (effect size)..: 0.899 
Lower 95% CI..............: 0.771 
Upper 95% CI..............: 1.047 
Z-value...................: -1.371396 
P-value...................: 0.1702514 
Hosmer and Lemeshow r^2...: 0.034389 
Cox and Snell r^2.........: 0.046454 
Nagelkerke's pseudo r^2...: 0.062002 
Sample size of AE DB......: 2388 
Sample size of model......: 614 
Missing data %............: 74.28811 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
     (Intercept)  SmokerCurrentyes  
          1.1229            0.4918  

Degrees of Freedom: 615 Total (i.e. Null);  614 Residual
Null Deviance:      642.7 
Residual Deviance: 637.2    AIC: 641.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1867   0.4317   0.6442   0.7516   1.0235  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.362e+01  1.696e+03   0.008   0.9936  
currentDF[, PROTEIN]       4.167e-03  9.006e-02   0.046   0.9631  
Age                        5.593e-03  1.253e-02   0.446   0.6554  
Gendermale                -3.436e-03  2.219e-01  -0.015   0.9876  
Hypertension.compositeyes  2.294e-03  3.016e-01   0.008   0.9939  
DiabetesStatusDiabetes     2.969e-01  2.630e-01   1.129   0.2589  
SmokerCurrentyes           5.129e-01  2.286e-01   2.244   0.0249 *
Med.Statin.LLDyes          1.524e-01  2.529e-01   0.603   0.5468  
Med.all.antiplateletyes    2.126e-01  3.455e-01   0.615   0.5383  
GFR_MDRD                   3.779e-03  5.528e-03   0.684   0.4942  
BMI                        3.788e-02  2.895e-02   1.308   0.1908  
CAD_history                2.453e-01  2.369e-01   1.036   0.3003  
Stroke_history             8.335e-02  2.201e-01   0.379   0.7050  
Peripheral.interv          1.403e-01  2.762e-01   0.508   0.6116  
stenose50-70%             -1.412e+01  1.696e+03  -0.008   0.9934  
stenose70-90%             -1.507e+01  1.696e+03  -0.009   0.9929  
stenose90-99%             -1.496e+01  1.696e+03  -0.009   0.9930  
stenose100% (Occlusion)    5.649e-01  1.918e+03   0.000   0.9998  
stenose70-99%              6.286e-01  2.399e+03   0.000   0.9998  
LDL_final                  1.174e-01  1.055e-01   1.112   0.2660  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 642.71  on 615  degrees of freedom
Residual deviance: 622.54  on 596  degrees of freedom
AIC: 662.54

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.004167 
Standard error............: 0.090058 
Odds ratio (effect size)..: 1.004 
Lower 95% CI..............: 0.842 
Upper 95% CI..............: 1.198 
Z-value...................: 0.046265 
P-value...................: 0.9630989 
Hosmer and Lemeshow r^2...: 0.031378 
Cox and Snell r^2.........: 0.032208 
Nagelkerke's pseudo r^2...: 0.049725 
Sample size of AE DB......: 2388 
Sample size of model......: 616 
Missing data %............: 74.20436 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Stroke_history + 
    Peripheral.interv + LDL_final, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
      (Intercept)         Gendermale     Stroke_history  Peripheral.interv          LDL_final  
          -0.1296             0.9069             0.4893            -0.5359             0.1945  

Degrees of Freedom: 615 Total (i.e. Null);  611 Residual
Null Deviance:      705.7 
Residual Deviance: 669.9    AIC: 679.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2582  -1.0691   0.6233   0.7802   1.3523  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                13.369868 623.666426   0.021   0.9829    
currentDF[, PROTEIN]        0.054158   0.085467   0.634   0.5263    
Age                         0.002498   0.012054   0.207   0.8359    
Gendermale                  0.948277   0.205225   4.621 3.83e-06 ***
Hypertension.compositeyes   0.185882   0.287940   0.646   0.5186    
DiabetesStatusDiabetes     -0.045731   0.241994  -0.189   0.8501    
SmokerCurrentyes            0.202464   0.211826   0.956   0.3392    
Med.Statin.LLDyes          -0.255734   0.259282  -0.986   0.3240    
Med.all.antiplateletyes     0.193744   0.342976   0.565   0.5721    
GFR_MDRD                   -0.002854   0.005456  -0.523   0.6008    
BMI                        -0.008758   0.026188  -0.334   0.7381    
CAD_history                 0.047658   0.223185   0.214   0.8309    
Stroke_history              0.470947   0.220512   2.136   0.0327 *  
Peripheral.interv          -0.594754   0.239320  -2.485   0.0129 *  
stenose50-70%             -13.706507 623.665074  -0.022   0.9825    
stenose70-90%             -13.169212 623.664944  -0.021   0.9832    
stenose90-99%             -13.284749 623.664938  -0.021   0.9830    
stenose100% (Occlusion)   -14.088904 623.665442  -0.023   0.9820    
stenose70-99%             -14.595709 623.666628  -0.023   0.9813    
LDL_final                   0.162951   0.102327   1.592   0.1113    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 705.67  on 615  degrees of freedom
Residual deviance: 662.08  on 596  degrees of freedom
AIC: 702.08

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.054158 
Standard error............: 0.085467 
Odds ratio (effect size)..: 1.056 
Lower 95% CI..............: 0.893 
Upper 95% CI..............: 1.248 
Z-value...................: 0.633675 
P-value...................: 0.526293 
Hosmer and Lemeshow r^2...: 0.06178 
Cox and Snell r^2.........: 0.068327 
Nagelkerke's pseudo r^2...: 0.100192 
Sample size of AE DB......: 2388 
Sample size of model......: 616 
Missing data %............: 74.20436 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + SmokerCurrent + Med.all.antiplatelet + Peripheral.interv, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age               Gendermale         SmokerCurrentyes  
               -1.73188                  0.14752                  0.02187                  0.62959                  0.30390  
Med.all.antiplateletyes        Peripheral.interv  
                0.51845                  0.32579  

Degrees of Freedom: 614 Total (i.e. Null);  608 Residual
Null Deviance:      809.9 
Residual Deviance: 786.7    AIC: 800.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0032  -1.2447   0.7716   0.9589   1.3876  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -1.9710605  1.8997910  -1.038 0.299496    
currentDF[, PROTEIN]       0.1228828  0.0774451   1.587 0.112579    
Age                        0.0166300  0.0108084   1.539 0.123897    
Gendermale                 0.6555481  0.1889490   3.469 0.000522 ***
Hypertension.compositeyes  0.1050767  0.2577038   0.408 0.683463    
DiabetesStatusDiabetes    -0.1861702  0.2167506  -0.859 0.390388    
SmokerCurrentyes           0.2894346  0.1915645   1.511 0.130814    
Med.Statin.LLDyes         -0.2219860  0.2264142  -0.980 0.326868    
Med.all.antiplateletyes    0.5140017  0.3042486   1.689 0.091140 .  
GFR_MDRD                  -0.0036757  0.0048703  -0.755 0.450417    
BMI                       -0.0002495  0.0236391  -0.011 0.991580    
CAD_history                0.2679931  0.2036027   1.316 0.188089    
Stroke_history             0.2994626  0.1924626   1.556 0.119719    
Peripheral.interv          0.3405722  0.2386829   1.427 0.153614    
stenose50-70%              0.4040515  1.4880029   0.272 0.785976    
stenose70-90%              0.4991339  1.4377983   0.347 0.728477    
stenose90-99%              0.5493762  1.4363202   0.382 0.702099    
stenose100% (Occlusion)    0.4420048  1.6386209   0.270 0.787359    
stenose70-99%             -0.3107490  2.0346938  -0.153 0.878615    
LDL_final                  0.0850495  0.0902429   0.942 0.345962    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 809.93  on 614  degrees of freedom
Residual deviance: 778.33  on 595  degrees of freedom
AIC: 818.33

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: 0.122883 
Standard error............: 0.077445 
Odds ratio (effect size)..: 1.131 
Lower 95% CI..............: 0.972 
Upper 95% CI..............: 1.316 
Z-value...................: 1.586708 
P-value...................: 0.1125789 
Hosmer and Lemeshow r^2...: 0.039017 
Cox and Snell r^2.........: 0.050086 
Nagelkerke's pseudo r^2...: 0.068418 
Sample size of AE DB......: 2388 
Sample size of model......: 615 
Missing data %............: 74.24623 

Analysis of MCP1_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + SmokerCurrent + Peripheral.interv + LDL_final, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age      SmokerCurrentyes     Peripheral.interv             LDL_final  
            -1.73495              -0.31223               0.02487               0.40727              -0.31242              -0.11567  

Degrees of Freedom: 635 Total (i.e. Null);  630 Residual
Null Deviance:      880.3 
Residual Deviance: 839.5    AIC: 851.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8203  -1.1443   0.7133   1.0820   1.7743  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                -2.186778   1.751653  -1.248  0.21188    
currentDF[, PROTEIN]       -0.313354   0.066094  -4.741 2.13e-06 ***
Age                         0.028386   0.010421   2.724  0.00645 ** 
Gendermale                  0.077519   0.185548   0.418  0.67610    
Hypertension.compositeyes   0.162429   0.253659   0.640  0.52195    
DiabetesStatusDiabetes     -0.117603   0.210834  -0.558  0.57698    
SmokerCurrentyes            0.437906   0.184123   2.378  0.01739 *  
Med.Statin.LLDyes          -0.263210   0.212928  -1.236  0.21640    
Med.all.antiplateletyes     0.171668   0.291815   0.588  0.55635    
GFR_MDRD                    0.003704   0.004551   0.814  0.41574    
BMI                         0.012750   0.022517   0.566  0.57124    
CAD_history                 0.032059   0.193000   0.166  0.86807    
Stroke_history             -0.126189   0.182798  -0.690  0.48999    
Peripheral.interv          -0.359200   0.221288  -1.623  0.10454    
stenose50-70%              -1.264011   1.313877  -0.962  0.33603    
stenose70-90%              -0.354338   1.252555  -0.283  0.77726    
stenose90-99%              -0.337422   1.251122  -0.270  0.78739    
stenose100% (Occlusion)     0.116115   1.519853   0.076  0.93910    
stenose70-99%             -15.425510 596.420676  -0.026  0.97937    
LDL_final                  -0.150771   0.087187  -1.729  0.08376 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 880.27  on 635  degrees of freedom
Residual deviance: 826.00  on 616  degrees of freedom
AIC: 866

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.313354 
Standard error............: 0.066094 
Odds ratio (effect size)..: 0.731 
Lower 95% CI..............: 0.642 
Upper 95% CI..............: 0.832 
Z-value...................: -4.741015 
P-value...................: 2.126505e-06 
Hosmer and Lemeshow r^2...: 0.061647 
Cox and Snell r^2.........: 0.081785 
Nagelkerke's pseudo r^2...: 0.109128 
Sample size of AE DB......: 2388 
Sample size of model......: 636 
Missing data %............: 73.36683 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerCurrent, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]      SmokerCurrentyes  
              0.8981               -0.2238                0.5088  

Degrees of Freedom: 637 Total (i.e. Null);  635 Residual
Null Deviance:      658.5 
Residual Deviance: 643.5    AIC: 649.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2910   0.3778   0.6175   0.7387   1.1095  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.342e+01  1.382e+03   0.010  0.99225   
currentDF[, PROTEIN]      -2.208e-01  7.866e-02  -2.807  0.00501 **
Age                        4.608e-03  1.243e-02   0.371  0.71083   
Gendermale                -1.078e-02  2.224e-01  -0.048  0.96133   
Hypertension.compositeyes -5.278e-02  3.030e-01  -0.174  0.86171   
DiabetesStatusDiabetes     2.626e-01  2.605e-01   1.008  0.31344   
SmokerCurrentyes           5.090e-01  2.287e-01   2.226  0.02604 * 
Med.Statin.LLDyes          1.414e-01  2.510e-01   0.563  0.57335   
Med.all.antiplateletyes    8.044e-02  3.422e-01   0.235  0.81417   
GFR_MDRD                   4.835e-03  5.415e-03   0.893  0.37194   
BMI                        3.947e-02  2.890e-02   1.366  0.17194   
CAD_history                2.561e-01  2.351e-01   1.089  0.27610   
Stroke_history             1.127e-01  2.197e-01   0.513  0.60798   
Peripheral.interv          1.576e-01  2.759e-01   0.571  0.56785   
stenose50-70%             -1.419e+01  1.382e+03  -0.010  0.99181   
stenose70-90%             -1.505e+01  1.382e+03  -0.011  0.99131   
stenose90-99%             -1.500e+01  1.382e+03  -0.011  0.99134   
stenose100% (Occlusion)    2.831e-01  1.649e+03   0.000  0.99986   
stenose70-99%              7.185e-01  2.188e+03   0.000  0.99974   
LDL_final                  1.570e-01  1.054e-01   1.489  0.13646   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 658.5  on 637  degrees of freedom
Residual deviance: 628.5  on 618  degrees of freedom
AIC: 668.5

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.220764 
Standard error............: 0.078656 
Odds ratio (effect size)..: 0.802 
Lower 95% CI..............: 0.687 
Upper 95% CI..............: 0.936 
Z-value...................: -2.806693 
P-value...................: 0.005005292 
Hosmer and Lemeshow r^2...: 0.045568 
Cox and Snell r^2.........: 0.045943 
Nagelkerke's pseudo r^2...: 0.071367 
Sample size of AE DB......: 2388 
Sample size of model......: 638 
Missing data %............: 73.28308 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv + LDL_final, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale        Stroke_history     Peripheral.interv             LDL_final  
             0.08208               0.10797               0.94650               0.42457              -0.48857               0.15348  

Degrees of Freedom: 637 Total (i.e. Null);  632 Residual
Null Deviance:      737.7 
Residual Deviance: 697.9    AIC: 709.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2927  -1.0871   0.6267   0.7942   1.3858  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                13.502340 501.501259   0.027   0.9785    
currentDF[, PROTEIN]        0.106038   0.070527   1.504   0.1327    
Age                         0.001382   0.011569   0.119   0.9049    
Gendermale                  0.995947   0.200571   4.966 6.85e-07 ***
Hypertension.compositeyes   0.211642   0.284536   0.744   0.4570    
DiabetesStatusDiabetes     -0.110556   0.233413  -0.474   0.6357    
SmokerCurrentyes            0.232019   0.207172   1.120   0.2627    
Med.Statin.LLDyes          -0.285167   0.252105  -1.131   0.2580    
Med.all.antiplateletyes     0.282374   0.325554   0.867   0.3857    
GFR_MDRD                   -0.005622   0.005182  -1.085   0.2780    
BMI                         0.001346   0.024744   0.054   0.9566    
CAD_history                 0.050724   0.217427   0.233   0.8155    
Stroke_history              0.398954   0.216374   1.844   0.0652 .  
Peripheral.interv          -0.546898   0.234383  -2.333   0.0196 *  
stenose50-70%             -13.715798 501.499552  -0.027   0.9782    
stenose70-90%             -13.286922 501.499395  -0.026   0.9789    
stenose90-99%             -13.413649 501.499389  -0.027   0.9787    
stenose100% (Occlusion)   -14.108936 501.499989  -0.028   0.9776    
stenose70-99%             -14.820496 501.501493  -0.030   0.9764    
LDL_final                   0.128934   0.098440   1.310   0.1903    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 737.67  on 637  degrees of freedom
Residual deviance: 688.27  on 618  degrees of freedom
AIC: 728.27

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.106038 
Standard error............: 0.070527 
Odds ratio (effect size)..: 1.112 
Lower 95% CI..............: 0.968 
Upper 95% CI..............: 1.277 
Z-value...................: 1.503511 
P-value...................: 0.1327074 
Hosmer and Lemeshow r^2...: 0.066965 
Cox and Snell r^2.........: 0.074504 
Nagelkerke's pseudo r^2...: 0.108713 
Sample size of AE DB......: 2388 
Sample size of model......: 638 
Missing data %............: 73.28308 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + CAD_history + 
    Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)                      Age               Gendermale         SmokerCurrentyes        Med.Statin.LLDyes  
               -1.73057                  0.01821                  0.60621                  0.34901                 -0.40268  
Med.all.antiplateletyes              CAD_history           Stroke_history        Peripheral.interv  
                0.60993                  0.30075                  0.34840                  0.39779  

Degrees of Freedom: 636 Total (i.e. Null);  628 Residual
Null Deviance:      837.7 
Residual Deviance: 806.3    AIC: 824.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9694  -1.2529   0.7654   0.9505   1.4993  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -2.236604   1.772434  -1.262 0.206991    
currentDF[, PROTEIN]      -0.088207   0.064866  -1.360 0.173883    
Age                        0.017937   0.010555   1.699 0.089230 .  
Gendermale                 0.654559   0.186699   3.506 0.000455 ***
Hypertension.compositeyes  0.042545   0.257261   0.165 0.868647    
DiabetesStatusDiabetes    -0.151501   0.212570  -0.713 0.476025    
SmokerCurrentyes           0.337455   0.188614   1.789 0.073594 .  
Med.Statin.LLDyes         -0.371866   0.223949  -1.660 0.096815 .  
Med.all.antiplateletyes    0.590621   0.291944   2.023 0.043066 *  
GFR_MDRD                  -0.002562   0.004687  -0.547 0.584607    
BMI                        0.007867   0.022713   0.346 0.729077    
CAD_history                0.301254   0.199352   1.511 0.130746    
Stroke_history             0.389073   0.191490   2.032 0.042172 *  
Peripheral.interv          0.397429   0.234324   1.696 0.089873 .  
stenose50-70%             -0.120217   1.318577  -0.091 0.927356    
stenose70-90%              0.122338   1.264558   0.097 0.922930    
stenose90-99%              0.191060   1.263050   0.151 0.879764    
stenose100% (Occlusion)   -0.036943   1.489262  -0.025 0.980210    
stenose70-99%             -0.906126   1.915305  -0.473 0.636144    
LDL_final                  0.071514   0.088157   0.811 0.417243    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 837.69  on 636  degrees of freedom
Residual deviance: 801.66  on 617  degrees of freedom
AIC: 841.66

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: -0.088207 
Standard error............: 0.064866 
Odds ratio (effect size)..: 0.916 
Lower 95% CI..............: 0.806 
Upper 95% CI..............: 1.04 
Z-value...................: -1.359831 
P-value...................: 0.1738834 
Hosmer and Lemeshow r^2...: 0.043019 
Cox and Snell r^2.........: 0.055001 
Nagelkerke's pseudo r^2...: 0.075186 
Sample size of AE DB......: 2388 
Sample size of model......: 637 
Missing data %............: 73.32496 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ SmokerCurrent + Med.Statin.LLD, 
    data = currentDF)

Coefficients:
      (Intercept)   SmokerCurrentyes  Med.Statin.LLDyes  
          0.03459            0.16805           -0.16826  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.92124 -0.73316 -0.00557  0.71379  3.03756 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                3.792704   1.329636   2.852  0.00462 **
currentDF[, TRAIT]        -0.061504   0.050080  -1.228  0.22030   
Age                       -0.009624   0.006942  -1.386  0.16658   
Gendermale                -0.159404   0.125754  -1.268  0.20587   
Hypertension.compositeyes  0.104571   0.162654   0.643  0.52074   
DiabetesStatusDiabetes    -0.132718   0.145494  -0.912  0.36235   
SmokerCurrentyes           0.109285   0.116428   0.939  0.34862   
Med.Statin.LLDyes         -0.278661   0.131209  -2.124  0.03445 * 
Med.all.antiplateletyes   -0.227481   0.234994  -0.968  0.33376   
GFR_MDRD                   0.001924   0.003229   0.596  0.55182   
BMI                       -0.017054   0.014966  -1.140  0.25533   
CAD_history                0.243533   0.123997   1.964  0.05039 . 
Stroke_history             0.102115   0.117514   0.869  0.38552   
Peripheral.interv         -0.116734   0.137714  -0.848  0.39726   
stenose50-70%             -2.456211   1.045001  -2.350  0.01936 * 
stenose70-90%             -2.333147   0.999077  -2.335  0.02014 * 
stenose90-99%             -2.375923   0.998637  -2.379  0.01794 * 
stenose100% (Occlusion)   -2.851865   1.098896  -2.595  0.00989 **
LDL_final                 -0.054180   0.056915  -0.952  0.34184   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9784 on 321 degrees of freedom
Multiple R-squared:  0.06494,   Adjusted R-squared:  0.01251 
F-statistic: 1.239 on 18 and 321 DF,  p-value: 0.2282

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: -0.061504 
Standard error............: 0.05008 
Odds ratio (effect size)..: 0.94 
Lower 95% CI..............: 0.852 
Upper 95% CI..............: 1.037 
T-value...................: -1.228115 
P-value...................: 0.2203034 
R^2.......................: 0.064943 
Adjusted r^2..............: 0.01251 
Sample size of AE DB......: 2388 
Sample size of model......: 340 
Missing data %............: 85.76214 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD, data = currentDF)

Coefficients:
      (Intercept)  Med.Statin.LLDyes  
           0.1105            -0.1671  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.94593 -0.72114 -0.00767  0.72157  2.95142 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                3.783029   1.343497   2.816  0.00517 **
currentDF[, TRAIT]         0.040659   0.053776   0.756  0.45016   
Age                       -0.009438   0.007120  -1.326  0.18592   
Gendermale                -0.147744   0.128474  -1.150  0.25101   
Hypertension.compositeyes  0.064319   0.163151   0.394  0.69368   
DiabetesStatusDiabetes    -0.127047   0.146769  -0.866  0.38735   
SmokerCurrentyes           0.097880   0.117339   0.834  0.40481   
Med.Statin.LLDyes         -0.257756   0.133795  -1.927  0.05493 . 
Med.all.antiplateletyes   -0.197395   0.237058  -0.833  0.40564   
GFR_MDRD                   0.001715   0.003269   0.525  0.60016   
BMI                       -0.019334   0.015123  -1.278  0.20201   
CAD_history                0.196926   0.125287   1.572  0.11699   
Stroke_history             0.081858   0.118994   0.688  0.49201   
Peripheral.interv         -0.090695   0.139342  -0.651  0.51559   
stenose50-70%             -2.414960   1.057550  -2.284  0.02306 * 
stenose70-90%             -2.286060   1.010233  -2.263  0.02431 * 
stenose90-99%             -2.289655   1.008776  -2.270  0.02389 * 
stenose100% (Occlusion)   -2.758852   1.109643  -2.486  0.01342 * 
LDL_final                 -0.053128   0.058295  -0.911  0.36279   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9889 on 319 degrees of freedom
Multiple R-squared:  0.05635,   Adjusted R-squared:  0.003105 
F-statistic: 1.058 on 18 and 319 DF,  p-value: 0.3942

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: 0.040659 
Standard error............: 0.053776 
Odds ratio (effect size)..: 1.041 
Lower 95% CI..............: 0.937 
Upper 95% CI..............: 1.157 
T-value...................: 0.756087 
P-value...................: 0.4501553 
R^2.......................: 0.056351 
Adjusted r^2..............: 0.003105 
Sample size of AE DB......: 2388 
Sample size of model......: 338 
Missing data %............: 85.8459 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Med.Statin.LLD, data = currentDF)

Coefficients:
      (Intercept)                Age  Med.Statin.LLDyes  
         0.753665          -0.009766          -0.170287  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.81606 -0.73816  0.00031  0.73756  3.08388 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                3.913497   1.364851   2.867  0.00442 **
currentDF[, TRAIT]        -0.073530   0.068216  -1.078  0.28191   
Age                       -0.011121   0.007153  -1.555  0.12101   
Gendermale                -0.131235   0.128403  -1.022  0.30754   
Hypertension.compositeyes  0.102116   0.166801   0.612  0.54085   
DiabetesStatusDiabetes    -0.124773   0.151385  -0.824  0.41045   
SmokerCurrentyes           0.108612   0.119294   0.910  0.36328   
Med.Statin.LLDyes         -0.249016   0.134561  -1.851  0.06517 . 
Med.all.antiplateletyes   -0.209457   0.240938  -0.869  0.38533   
GFR_MDRD                   0.001910   0.003328   0.574  0.56652   
BMI                       -0.019283   0.015234  -1.266  0.20652   
CAD_history                0.201687   0.126238   1.598  0.11113   
Stroke_history             0.106492   0.119862   0.888  0.37498   
Peripheral.interv         -0.092475   0.140901  -0.656  0.51211   
stenose50-70%             -2.466777   1.062496  -2.322  0.02089 * 
stenose70-90%             -2.346616   1.015922  -2.310  0.02155 * 
stenose90-99%             -2.368477   1.014795  -2.334  0.02023 * 
stenose100% (Occlusion)   -2.720515   1.144280  -2.377  0.01803 * 
LDL_final                 -0.055201   0.058274  -0.947  0.34423   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.991 on 313 degrees of freedom
Multiple R-squared:  0.05833,   Adjusted R-squared:  0.004181 
F-statistic: 1.077 on 18 and 313 DF,  p-value: 0.3742

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.07353 
Standard error............: 0.068216 
Odds ratio (effect size)..: 0.929 
Lower 95% CI..............: 0.813 
Upper 95% CI..............: 1.062 
T-value...................: -1.077905 
P-value...................: 0.281906 
R^2.......................: 0.058334 
Adjusted r^2..............: 0.004181 
Sample size of AE DB......: 2388 
Sample size of model......: 332 
Missing data %............: 86.09715 

Analysis of MCP1_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    Hypertension.composite + Med.all.antiplatelet + LDL_final, 
    data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                 Gendermale  Hypertension.compositeyes    Med.all.antiplateletyes  
                  -0.7615                     0.1160                     0.4242                    -0.2413                     0.4022  
                LDL_final  
                   0.1261  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0118 -0.6421  0.0530  0.6167  2.7322 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.550180   1.331823   1.164  0.24525   
currentDF[, TRAIT]         0.108718   0.049706   2.187  0.02940 * 
Age                       -0.010664   0.006893  -1.547  0.12277   
Gendermale                 0.399090   0.123790   3.224  0.00139 **
Hypertension.compositeyes -0.256160   0.158543  -1.616  0.10708   
DiabetesStatusDiabetes    -0.120459   0.143222  -0.841  0.40090   
SmokerCurrentyes          -0.026308   0.115098  -0.229  0.81934   
Med.Statin.LLDyes         -0.160799   0.130582  -1.231  0.21902   
Med.all.antiplateletyes    0.304046   0.223821   1.358  0.17522   
GFR_MDRD                  -0.002907   0.003184  -0.913  0.36182   
BMI                       -0.007932   0.014201  -0.559  0.57683   
CAD_history                0.194780   0.124501   1.564  0.11863   
Stroke_history             0.141896   0.116406   1.219  0.22369   
Peripheral.interv         -0.139426   0.141656  -0.984  0.32568   
stenose50-70%             -0.817147   1.069218  -0.764  0.44525   
stenose70-90%             -0.926314   1.024033  -0.905  0.36633   
stenose90-99%             -0.919539   1.023909  -0.898  0.36978   
stenose100% (Occlusion)   -1.623694   1.150886  -1.411  0.15921   
LDL_final                  0.100281   0.056049   1.789  0.07447 . 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.004 on 342 degrees of freedom
Multiple R-squared:  0.103, Adjusted R-squared:  0.05578 
F-statistic: 2.182 on 18 and 342 DF,  p-value: 0.003837

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.108718 
Standard error............: 0.049706 
Odds ratio (effect size)..: 1.115 
Lower 95% CI..............: 1.011 
Upper 95% CI..............: 1.229 
T-value...................: 2.187222 
P-value...................: 0.0294031 
R^2.......................: 0.102995 
Adjusted r^2..............: 0.055784 
Sample size of AE DB......: 2388 
Sample size of model......: 361 
Missing data %............: 84.88275 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + Med.all.antiplatelet + 
    CAD_history + Stroke_history + LDL_final, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale  Hypertension.compositeyes  
                 -0.16113                   -0.17912                   -0.01124                    0.38951                   -0.21541  
  Med.all.antiplateletyes                CAD_history             Stroke_history                  LDL_final  
                  0.34350                    0.22827                    0.16045                    0.17107  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.77475 -0.66379  0.03745  0.63641  2.44650 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.761592   1.309017   1.346  0.17928    
currentDF[, TRAIT]        -0.175444   0.051692  -3.394  0.00077 ***
Age                       -0.015241   0.006873  -2.218  0.02725 *  
Gendermale                 0.367995   0.122914   2.994  0.00296 ** 
Hypertension.compositeyes -0.223901   0.154761  -1.447  0.14889    
DiabetesStatusDiabetes    -0.109872   0.141026  -0.779  0.43647    
SmokerCurrentyes          -0.037317   0.113021  -0.330  0.74147    
Med.Statin.LLDyes         -0.120464   0.129434  -0.931  0.35267    
Med.all.antiplateletyes    0.274163   0.219821   1.247  0.21318    
GFR_MDRD                  -0.003006   0.003132  -0.960  0.33782    
BMI                       -0.005209   0.013963  -0.373  0.70932    
CAD_history                0.254655   0.122268   2.083  0.03802 *  
Stroke_history             0.154659   0.114573   1.350  0.17795    
Peripheral.interv         -0.150381   0.139883  -1.075  0.28312    
stenose50-70%             -0.846437   1.051986  -0.805  0.42161    
stenose70-90%             -0.983213   1.007553  -0.976  0.32984    
stenose90-99%             -1.000154   1.006474  -0.994  0.32107    
stenose100% (Occlusion)   -1.745817   1.130964  -1.544  0.12360    
LDL_final                  0.142069   0.055893   2.542  0.01147 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9871 on 340 degrees of freedom
Multiple R-squared:  0.1247,    Adjusted R-squared:  0.07836 
F-statistic: 2.691 on 18 and 340 DF,  p-value: 0.0002653

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.175444 
Standard error............: 0.051692 
Odds ratio (effect size)..: 0.839 
Lower 95% CI..............: 0.758 
Upper 95% CI..............: 0.929 
T-value...................: -3.393995 
P-value...................: 0.0007701751 
R^2.......................: 0.124699 
Adjusted r^2..............: 0.07836 
Sample size of AE DB......: 2388 
Sample size of model......: 359 
Missing data %............: 84.9665 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + Med.all.antiplatelet + 
    LDL_final, data = currentDF)

Coefficients:
            (Intercept)               Gendermale  Med.all.antiplateletyes                LDL_final  
                -0.9367                   0.4562                   0.3756                   0.1249  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.06281 -0.66297  0.04826  0.64889  2.70481 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.652265   1.356456   1.218 0.224053    
currentDF[, TRAIT]        -0.082322   0.065687  -1.253 0.210989    
Age                       -0.010687   0.007027  -1.521 0.129238    
Gendermale                 0.421397   0.126183   3.340 0.000934 ***
Hypertension.compositeyes -0.198725   0.162370  -1.224 0.221848    
DiabetesStatusDiabetes    -0.080616   0.147908  -0.545 0.586088    
SmokerCurrentyes          -0.023542   0.117495  -0.200 0.841314    
Med.Statin.LLDyes         -0.183933   0.133849  -1.374 0.170302    
Med.all.antiplateletyes    0.264553   0.225729   1.172 0.242032    
GFR_MDRD                  -0.001982   0.003272  -0.606 0.544998    
BMI                       -0.006037   0.014481  -0.417 0.677042    
CAD_history                0.220242   0.126669   1.739 0.083004 .  
Stroke_history             0.140083   0.118301   1.184 0.237203    
Peripheral.interv         -0.116106   0.144786  -0.802 0.423171    
stenose50-70%             -1.032080   1.081916  -0.954 0.340803    
stenose70-90%             -1.074798   1.036998  -1.036 0.300739    
stenose90-99%             -1.095509   1.035980  -1.057 0.291063    
stenose100% (Occlusion)   -1.850483   1.164954  -1.588 0.113126    
LDL_final                  0.085880   0.057439   1.495 0.135820    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.013 on 335 degrees of freedom
Multiple R-squared:  0.08945,   Adjusted R-squared:  0.04053 
F-statistic: 1.828 on 18 and 335 DF,  p-value: 0.0212

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.082322 
Standard error............: 0.065687 
Odds ratio (effect size)..: 0.921 
Lower 95% CI..............: 0.81 
Upper 95% CI..............: 1.048 
T-value...................: -1.253249 
P-value...................: 0.2109892 
R^2.......................: 0.089453 
Adjusted r^2..............: 0.040529 
Sample size of AE DB......: 2388 
Sample size of model......: 354 
Missing data %............: 85.17588 

Analysis of IL6_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + SmokerCurrent + 
    BMI + Stroke_history, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]    SmokerCurrentyes                 BMI      Stroke_history  
           0.43172             0.08968             0.12696            -0.01892             0.20719  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3185 -0.6385  0.0175  0.6268  2.8088 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)                0.956669   0.814353   1.175   0.2406  
currentDF[, TRAIT]         0.087966   0.040062   2.196   0.0285 *
Age                       -0.002904   0.004882  -0.595   0.5522  
Gendermale                -0.009734   0.088746  -0.110   0.9127  
Hypertension.compositeyes -0.054264   0.122717  -0.442   0.6585  
DiabetesStatusDiabetes    -0.076409   0.100157  -0.763   0.4458  
SmokerCurrentyes           0.125108   0.086655   1.444   0.1493  
Med.Statin.LLDyes         -0.111106   0.100331  -1.107   0.2686  
Med.all.antiplateletyes    0.012174   0.141146   0.086   0.9313  
GFR_MDRD                  -0.003536   0.002146  -1.648   0.0999 .
BMI                       -0.021052   0.010734  -1.961   0.0503 .
CAD_history               -0.056364   0.092349  -0.610   0.5419  
Stroke_history             0.187557   0.086643   2.165   0.0308 *
Peripheral.interv         -0.051133   0.105339  -0.485   0.6276  
stenose50-70%              0.012852   0.603337   0.021   0.9830  
stenose70-90%              0.269820   0.576599   0.468   0.6400  
stenose90-99%              0.105576   0.575918   0.183   0.8546  
stenose100% (Occlusion)    0.179979   0.688069   0.262   0.7937  
stenose70-99%             -0.916356   0.912926  -1.004   0.3159  
LDL_final                 -0.004545   0.040502  -0.112   0.9107  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9848 on 602 degrees of freedom
Multiple R-squared:  0.04836,   Adjusted R-squared:  0.01832 
F-statistic:  1.61 on 19 and 602 DF,  p-value: 0.04862

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.087966 
Standard error............: 0.040062 
Odds ratio (effect size)..: 1.092 
Lower 95% CI..............: 1.009 
Upper 95% CI..............: 1.181 
T-value...................: 2.19572 
P-value...................: 0.02849284 
R^2.......................: 0.048358 
Adjusted r^2..............: 0.018322 
Sample size of AE DB......: 2388 
Sample size of model......: 622 
Missing data %............: 73.9531 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + SmokerCurrent + 
    BMI + Stroke_history, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]    SmokerCurrentyes                 BMI      Stroke_history  
           0.38376            -0.16631             0.14266            -0.01639             0.19343  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.04985 -0.64800  0.01386  0.63069  2.55755 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.102447   0.806200   1.367   0.1720    
currentDF[, TRAIT]        -0.173938   0.040608  -4.283 2.14e-05 ***
Age                       -0.006913   0.004890  -1.414   0.1580    
Gendermale                -0.059486   0.089220  -0.667   0.5052    
Hypertension.compositeyes -0.045232   0.121514  -0.372   0.7099    
DiabetesStatusDiabetes    -0.066154   0.099234  -0.667   0.5053    
SmokerCurrentyes           0.118193   0.085988   1.375   0.1698    
Med.Statin.LLDyes         -0.100904   0.099815  -1.011   0.3125    
Med.all.antiplateletyes   -0.025880   0.139663  -0.185   0.8531    
GFR_MDRD                  -0.003067   0.002130  -1.440   0.1505    
BMI                       -0.018815   0.010660  -1.765   0.0781 .  
CAD_history               -0.032078   0.091688  -0.350   0.7266    
Stroke_history             0.185386   0.085884   2.159   0.0313 *  
Peripheral.interv         -0.055377   0.105115  -0.527   0.5985    
stenose50-70%              0.040206   0.597216   0.067   0.9463    
stenose70-90%              0.333147   0.571076   0.583   0.5599    
stenose90-99%              0.179920   0.570433   0.315   0.7526    
stenose100% (Occlusion)    0.203774   0.680636   0.299   0.7647    
stenose70-99%             -0.753656   0.904215  -0.833   0.4049    
LDL_final                  0.008340   0.040306   0.207   0.8361    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9747 on 598 degrees of freedom
Multiple R-squared:  0.06837,   Adjusted R-squared:  0.03877 
F-statistic:  2.31 on 19 and 598 DF,  p-value: 0.001317

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.173938 
Standard error............: 0.040608 
Odds ratio (effect size)..: 0.84 
Lower 95% CI..............: 0.776 
Upper 95% CI..............: 0.91 
T-value...................: -4.283342 
P-value...................: 2.144872e-05 
R^2.......................: 0.068374 
Adjusted r^2..............: 0.038774 
Sample size of AE DB......: 2388 
Sample size of model......: 618 
Missing data %............: 74.1206 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + SmokerCurrent + 
    GFR_MDRD + BMI + Stroke_history, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]    SmokerCurrentyes            GFR_MDRD                 BMI      Stroke_history  
          0.645933           -0.073470            0.151037           -0.003299           -0.019370            0.223348  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2477 -0.6315  0.0154  0.6292  2.7442 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)                0.915562   0.833720   1.098   0.2726  
currentDF[, TRAIT]        -0.060043   0.044518  -1.349   0.1780  
Age                       -0.002646   0.005072  -0.522   0.6020  
Gendermale                 0.006226   0.091559   0.068   0.9458  
Hypertension.compositeyes -0.026768   0.129442  -0.207   0.8362  
DiabetesStatusDiabetes    -0.082035   0.106538  -0.770   0.4416  
SmokerCurrentyes           0.129686   0.090901   1.427   0.1542  
Med.Statin.LLDyes         -0.102693   0.104321  -0.984   0.3253  
Med.all.antiplateletyes    0.034800   0.147180   0.236   0.8132  
GFR_MDRD                  -0.004247   0.002270  -1.871   0.0619 .
BMI                       -0.019937   0.011131  -1.791   0.0738 .
CAD_history               -0.058081   0.096857  -0.600   0.5490  
Stroke_history             0.204024   0.090661   2.250   0.0248 *
Peripheral.interv         -0.088141   0.112268  -0.785   0.4327  
stenose50-70%             -0.120730   0.611812  -0.197   0.8436  
stenose70-90%              0.168620   0.582906   0.289   0.7725  
stenose90-99%              0.027030   0.581958   0.046   0.9630  
stenose100% (Occlusion)    0.087610   0.694550   0.126   0.8997  
stenose70-99%             -1.032726   1.170442  -0.882   0.3780  
LDL_final                  0.013615   0.042281   0.322   0.7476  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9932 on 564 degrees of freedom
Multiple R-squared:  0.04632,   Adjusted R-squared:  0.01419 
F-statistic: 1.442 on 19 and 564 DF,  p-value: 0.1012

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.060043 
Standard error............: 0.044518 
Odds ratio (effect size)..: 0.942 
Lower 95% CI..............: 0.863 
Upper 95% CI..............: 1.028 
T-value...................: -1.348739 
P-value...................: 0.1779623 
R^2.......................: 0.04632 
Adjusted r^2..............: 0.014193 
Sample size of AE DB......: 2388 
Sample size of model......: 584 
Missing data %............: 75.54439 

Analysis of IL6R_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD + Peripheral.interv, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD   Peripheral.interv  
          1.341530            0.110321           -0.009911           -0.288745           -0.004870            0.266781  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2981 -0.6084 -0.0185  0.6169  2.8340 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.427044   0.897757   1.590  0.11246   
currentDF[, TRAIT]         0.108478   0.039229   2.765  0.00586 **
Age                       -0.009565   0.004928  -1.941  0.05274 . 
Gendermale                -0.033733   0.087882  -0.384  0.70123   
Hypertension.compositeyes  0.062434   0.119466   0.523  0.60144   
DiabetesStatusDiabetes    -0.119784   0.100152  -1.196  0.23217   
SmokerCurrentyes           0.038240   0.086309   0.443  0.65789   
Med.Statin.LLDyes         -0.285726   0.100574  -2.841  0.00465 **
Med.all.antiplateletyes   -0.116713   0.142200  -0.821  0.41211   
GFR_MDRD                  -0.005023   0.002183  -2.300  0.02177 * 
BMI                       -0.013214   0.010896  -1.213  0.22570   
CAD_history               -0.028311   0.091591  -0.309  0.75735   
Stroke_history             0.030194   0.086143   0.351  0.72608   
Peripheral.interv          0.246999   0.105103   2.350  0.01910 * 
stenose50-70%              0.061338   0.719052   0.085  0.93205   
stenose70-90%              0.293751   0.696861   0.422  0.67352   
stenose90-99%              0.380202   0.696101   0.546  0.58514   
stenose100% (Occlusion)   -0.089389   0.789470  -0.113  0.90989   
stenose70-99%             -1.096271   0.985446  -1.112  0.26639   
LDL_final                  0.006939   0.040808   0.170  0.86504   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9713 on 596 degrees of freedom
Multiple R-squared:  0.07061,   Adjusted R-squared:  0.04099 
F-statistic: 2.383 on 19 and 596 DF,  p-value: 0.0008684

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.108478 
Standard error............: 0.039229 
Odds ratio (effect size)..: 1.115 
Lower 95% CI..............: 1.032 
Upper 95% CI..............: 1.204 
T-value...................: 2.76527 
P-value...................: 0.005863709 
R^2.......................: 0.070614 
Adjusted r^2..............: 0.040986 
Sample size of AE DB......: 2388 
Sample size of model......: 616 
Missing data %............: 74.20436 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Med.Statin.LLD + GFR_MDRD + 
    Peripheral.interv, data = currentDF)

Coefficients:
      (Intercept)                Age  Med.Statin.LLDyes           GFR_MDRD  Peripheral.interv  
         1.346173          -0.010035          -0.298570          -0.004661           0.264690  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4215 -0.6309 -0.0031  0.6277  2.8688 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.4259326  0.9036264   1.578   0.1151   
currentDF[, TRAIT]         0.0451999  0.0409217   1.105   0.2698   
Age                       -0.0094317  0.0050198  -1.879   0.0608 . 
Gendermale                 0.0053504  0.0896154   0.060   0.9524   
Hypertension.compositeyes  0.0684131  0.1202476   0.569   0.5696   
DiabetesStatusDiabetes    -0.1253518  0.1008768  -1.243   0.2145   
SmokerCurrentyes           0.0244030  0.0870966   0.280   0.7794   
Med.Statin.LLDyes         -0.3010755  0.1017199  -2.960   0.0032 **
Med.all.antiplateletyes   -0.1322906  0.1430097  -0.925   0.3553   
GFR_MDRD                  -0.0048356  0.0022028  -2.195   0.0285 * 
BMI                       -0.0122421  0.0110041  -1.113   0.2664   
CAD_history               -0.0269855  0.0924033  -0.292   0.7704   
Stroke_history             0.0404923  0.0868126   0.466   0.6411   
Peripheral.interv          0.2464366  0.1066174   2.311   0.0212 * 
stenose50-70%              0.0201160  0.7239258   0.028   0.9778   
stenose70-90%              0.2720544  0.7017170   0.388   0.6984   
stenose90-99%              0.3421500  0.7010877   0.488   0.6257   
stenose100% (Occlusion)   -0.1850207  0.7946637  -0.233   0.8160   
stenose70-99%             -1.1033831  0.9923601  -1.112   0.2666   
LDL_final                  0.0001877  0.0413000   0.005   0.9964   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9772 on 592 degrees of freedom
Multiple R-squared:  0.05852,   Adjusted R-squared:  0.02831 
F-statistic: 1.937 on 19 and 592 DF,  p-value: 0.009947

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: 0.0452 
Standard error............: 0.040922 
Odds ratio (effect size)..: 1.046 
Lower 95% CI..............: 0.966 
Upper 95% CI..............: 1.134 
T-value...................: 1.104546 
P-value...................: 0.269805 
R^2.......................: 0.058524 
Adjusted r^2..............: 0.028307 
Sample size of AE DB......: 2388 
Sample size of model......: 612 
Missing data %............: 74.37186 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD + Peripheral.interv, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes            GFR_MDRD   Peripheral.interv  
          1.401930            0.070985           -0.010425           -0.313222           -0.004864            0.237237  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3090 -0.6200 -0.0146  0.6457  2.8167 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                1.509171   0.924131   1.633  0.10302   
currentDF[, TRAIT]         0.067452   0.045210   1.492  0.13627   
Age                       -0.010954   0.005160  -2.123  0.03422 * 
Gendermale                -0.030480   0.091431  -0.333  0.73899   
Hypertension.compositeyes  0.052927   0.127452   0.415  0.67810   
DiabetesStatusDiabetes    -0.137947   0.107477  -1.283  0.19985   
SmokerCurrentyes          -0.005957   0.091386  -0.065  0.94805   
Med.Statin.LLDyes         -0.302578   0.105563  -2.866  0.00431 **
Med.all.antiplateletyes   -0.106337   0.149561  -0.711  0.47739   
GFR_MDRD                  -0.005046   0.002339  -2.157  0.03143 * 
BMI                       -0.012429   0.011509  -1.080  0.28064   
CAD_history               -0.038156   0.096794  -0.394  0.69359   
Stroke_history             0.024410   0.090867   0.269  0.78831   
Peripheral.interv          0.225570   0.112822   1.999  0.04606 * 
stenose50-70%              0.068589   0.733201   0.094  0.92550   
stenose70-90%              0.326386   0.708414   0.461  0.64517   
stenose90-99%              0.395119   0.707544   0.558  0.57677   
stenose100% (Occlusion)   -0.102343   0.802434  -0.128  0.89856   
stenose70-99%             -1.253510   1.232215  -1.017  0.30946   
LDL_final                  0.009660   0.042947   0.225  0.82212   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9861 on 557 degrees of freedom
Multiple R-squared:  0.06024,   Adjusted R-squared:  0.02818 
F-statistic: 1.879 on 19 and 557 DF,  p-value: 0.01347

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: 0.067452 
Standard error............: 0.04521 
Odds ratio (effect size)..: 1.07 
Lower 95% CI..............: 0.979 
Upper 95% CI..............: 1.169 
T-value...................: 1.49198 
P-value...................: 0.1362705 
R^2.......................: 0.060239 
Adjusted r^2..............: 0.028183 
Sample size of AE DB......: 2388 
Sample size of model......: 577 
Missing data %............: 75.83752 

Analysis of MCP1_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + Hypertension.composite + 
    Med.Statin.LLD + Stroke_history + LDL_final, data = currentDF)

Coefficients:
              (Intercept)                 Gendermale  Hypertension.compositeyes          Med.Statin.LLDyes             Stroke_history  
                  -0.1273                     0.1474                    -0.2463                    -0.1445                     0.1993  
                LDL_final  
                   0.1006  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.9304 -0.7061 -0.0780  0.6314  3.2286 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)  
(Intercept)                0.291415   0.850113   0.343   0.7319  
currentDF[, TRAIT]        -0.049187   0.041051  -1.198   0.2313  
Age                       -0.003564   0.005088  -0.700   0.4839  
Gendermale                 0.158377   0.091362   1.734   0.0835 .
Hypertension.compositeyes -0.244154   0.125233  -1.950   0.0517 .
DiabetesStatusDiabetes    -0.068577   0.103560  -0.662   0.5081  
SmokerCurrentyes          -0.074855   0.089571  -0.836   0.4036  
Med.Statin.LLDyes         -0.159345   0.104268  -1.528   0.1270  
Med.all.antiplateletyes   -0.108230   0.145261  -0.745   0.4565  
GFR_MDRD                  -0.002234   0.002227  -1.003   0.3161  
BMI                       -0.008739   0.011066  -0.790   0.4300  
CAD_history                0.071133   0.094850   0.750   0.4536  
Stroke_history             0.213464   0.090021   2.371   0.0180 *
Peripheral.interv          0.065349   0.109169   0.599   0.5497  
stenose50-70%              0.098476   0.631189   0.156   0.8761  
stenose70-90%              0.387475   0.603162   0.642   0.5208  
stenose90-99%              0.271932   0.602534   0.451   0.6519  
stenose100% (Occlusion)   -0.341989   0.719678  -0.475   0.6348  
stenose70-99%              0.492227   0.954935   0.515   0.6064  
LDL_final                  0.105914   0.042067   2.518   0.0121 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.03 on 618 degrees of freedom
Multiple R-squared:  0.05515,   Adjusted R-squared:  0.02611 
F-statistic: 1.899 on 19 and 618 DF,  p-value: 0.01201

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: -0.049187 
Standard error............: 0.041051 
Odds ratio (effect size)..: 0.952 
Lower 95% CI..............: 0.878 
Upper 95% CI..............: 1.032 
T-value...................: -1.198196 
P-value...................: 0.2313002 
R^2.......................: 0.055154 
Adjusted r^2..............: 0.026106 
Sample size of AE DB......: 2388 
Sample size of model......: 638 
Missing data %............: 73.28308 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Hypertension.composite + 
    Stroke_history + LDL_final, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]  Hypertension.compositeyes             Stroke_history                  LDL_final  
                  -0.1782                    -0.1303                    -0.2508                     0.1852                     0.1236  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.6887 -0.6991 -0.0832  0.6479  3.2672 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.320457   0.847551   0.378  0.70549   
currentDF[, TRAIT]        -0.123250   0.042025  -2.933  0.00349 **
Age                       -0.005426   0.005127  -1.058  0.29029   
Gendermale                 0.106151   0.092455   1.148  0.25136   
Hypertension.compositeyes -0.244452   0.124883  -1.957  0.05075 . 
DiabetesStatusDiabetes    -0.062338   0.103312  -0.603  0.54647   
SmokerCurrentyes          -0.072628   0.089528  -0.811  0.41755   
Med.Statin.LLDyes         -0.148656   0.104496  -1.423  0.15536   
Med.all.antiplateletyes   -0.119560   0.144832  -0.826  0.40940   
GFR_MDRD                  -0.002072   0.002227  -0.931  0.35241   
BMI                       -0.007669   0.011068  -0.693  0.48861   
CAD_history                0.075659   0.094760   0.798  0.42493   
Stroke_history             0.205614   0.089880   2.288  0.02250 * 
Peripheral.interv          0.067705   0.109692   0.617  0.53732   
stenose50-70%              0.167650   0.629251   0.266  0.79000   
stenose70-90%              0.459421   0.601627   0.764  0.44538   
stenose90-99%              0.359549   0.601076   0.598  0.54994   
stenose100% (Occlusion)   -0.233259   0.716966  -0.325  0.74503   
stenose70-99%              0.599918   0.952635   0.630  0.52909   
LDL_final                  0.114486   0.042160   2.716  0.00680 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.027 on 614 degrees of freedom
Multiple R-squared:  0.06606,   Adjusted R-squared:  0.03716 
F-statistic: 2.286 on 19 and 614 DF,  p-value: 0.001497

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.12325 
Standard error............: 0.042025 
Odds ratio (effect size)..: 0.884 
Lower 95% CI..............: 0.814 
Upper 95% CI..............: 0.96 
T-value...................: -2.932747 
P-value...................: 0.003485007 
R^2.......................: 0.066063 
Adjusted r^2..............: 0.037163 
Sample size of AE DB......: 2388 
Sample size of model......: 634 
Missing data %............: 73.45059 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    Hypertension.composite + GFR_MDRD + Stroke_history + LDL_final, 
    data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                 Gendermale  Hypertension.compositeyes                   GFR_MDRD  
                -0.187163                  -0.136560                   0.195759                  -0.227145                  -0.003343  
           Stroke_history                  LDL_final  
                 0.210043                   0.141315  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    LDL_final, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.7564 -0.7060 -0.0830  0.6373  3.3215 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.278174   0.861772   0.323  0.74697   
currentDF[, TRAIT]        -0.131364   0.045384  -2.894  0.00394 **
Age                       -0.002927   0.005233  -0.559  0.57613   
Gendermale                 0.185450   0.093464   1.984  0.04771 * 
Hypertension.compositeyes -0.202835   0.131290  -1.545  0.12291   
DiabetesStatusDiabetes    -0.057482   0.108946  -0.528  0.59796   
SmokerCurrentyes          -0.044276   0.093160  -0.475  0.63477   
Med.Statin.LLDyes         -0.155652   0.107545  -1.447  0.14835   
Med.all.antiplateletyes   -0.066229   0.149739  -0.442  0.65844   
GFR_MDRD                  -0.002916   0.002332  -1.250  0.21165   
BMI                       -0.010907   0.011460  -0.952  0.34161   
CAD_history                0.089160   0.098346   0.907  0.36500   
Stroke_history             0.229929   0.093253   2.466  0.01397 * 
Peripheral.interv          0.029074   0.115049   0.253  0.80058   
stenose50-70%             -0.066620   0.633787  -0.105  0.91632   
stenose70-90%              0.276392   0.603793   0.458  0.64730   
stenose90-99%              0.171897   0.602917   0.285  0.77566   
stenose100% (Occlusion)   -0.398319   0.719404  -0.554  0.58001   
stenose70-99%              0.622479   1.212095   0.514  0.60776   
LDL_final                  0.122158   0.043471   2.810  0.00512 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.029 on 579 degrees of freedom
Multiple R-squared:  0.07479,   Adjusted R-squared:  0.04443 
F-statistic: 2.463 on 19 and 579 DF,  p-value: 0.0005535

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.131364 
Standard error............: 0.045384 
Odds ratio (effect size)..: 0.877 
Lower 95% CI..............: 0.802 
Upper 95% CI..............: 0.958 
T-value...................: -2.894467 
P-value...................: 0.003941151 
R^2.......................: 0.074786 
Adjusted r^2..............: 0.044425 
Sample size of AE DB......: 2388 
Sample size of model......: 599 
Missing data %............: 74.91625 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    Peripheral.interv + LDL_final, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes          Peripheral.interv                  LDL_final  
                   0.1729                     0.7282                    -0.4273                    -0.1618  

Degrees of Freedom: 340 Total (i.e. Null);  337 Residual
Null Deviance:      468.3 
Residual Deviance: 457.8    AIC: 465.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8654  -1.2136   0.7951   1.0480   1.7633  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -15.267781 535.414752  -0.029   0.9773  
currentDF[, PROTEIN]        0.163515   0.117905   1.387   0.1655  
Age                         0.010455   0.014857   0.704   0.4816  
Gendermale                 -0.035222   0.266332  -0.132   0.8948  
Hypertension.compositeyes   0.786773   0.346070   2.273   0.0230 *
DiabetesStatusDiabetes     -0.344363   0.306681  -1.123   0.2615  
SmokerCurrentyes            0.053976   0.246235   0.219   0.8265  
Med.Statin.LLDyes          -0.175548   0.281406  -0.624   0.5327  
Med.all.antiplateletyes     0.893264   0.512220   1.744   0.0812 .
GFR_MDRD                   -0.006645   0.006908  -0.962   0.3361  
BMI                         0.021593   0.031868   0.678   0.4981  
CAD_history                 0.085080   0.263727   0.323   0.7470  
Stroke_history             -0.294773   0.250322  -1.178   0.2390  
Peripheral.interv          -0.398990   0.287559  -1.388   0.1653  
stenose50-70%              14.702266 535.411824   0.027   0.9781  
stenose70-90%              14.250535 535.411403   0.027   0.9788  
stenose90-99%              13.920079 535.411392   0.026   0.9793  
stenose100% (Occlusion)    15.923187 535.412811   0.030   0.9763  
LDL_final                  -0.183427   0.121427  -1.511   0.1309  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 468.26  on 340  degrees of freedom
Residual deviance: 443.77  on 322  degrees of freedom
AIC: 481.77

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.163515 
Standard error............: 0.117905 
Odds ratio (effect size)..: 1.178 
Lower 95% CI..............: 0.935 
Upper 95% CI..............: 1.484 
Z-value...................: 1.386835 
P-value...................: 0.1654921 
Hosmer and Lemeshow r^2...: 0.05229 
Cox and Snell r^2.........: 0.069286 
Nagelkerke's pseudo r^2...: 0.09279 
Sample size of AE DB......: 2388 
Sample size of model......: 341 
Missing data %............: 85.72027 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)            stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
              1.757e+01                1.794e-08               -1.602e+01               -1.639e+01                1.752e-08  

Degrees of Freedom: 339 Total (i.e. Null);  335 Residual
Null Deviance:      343 
Residual Deviance: 333.4    AIC: 343.4

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.12359   0.00031   0.59757   0.72715   1.08348  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)
(Intercept)                1.825e+01  3.956e+03   0.005    0.996
currentDF[, PROTEIN]      -1.729e-01  1.444e-01  -1.198    0.231
Age                        6.532e-03  1.853e-02   0.353    0.724
Gendermale                -3.604e-01  3.433e-01  -1.050    0.294
Hypertension.compositeyes  3.197e-01  4.074e-01   0.785    0.433
DiabetesStatusDiabetes     3.033e-01  4.040e-01   0.751    0.453
SmokerCurrentyes           3.532e-01  3.085e-01   1.145    0.252
Med.Statin.LLDyes         -1.702e-01  3.466e-01  -0.491    0.623
Med.all.antiplateletyes    7.388e-01  5.520e-01   1.338    0.181
GFR_MDRD                  -5.268e-03  8.525e-03  -0.618    0.537
BMI                       -2.169e-02  3.918e-02  -0.554    0.580
CAD_history               -5.403e-02  3.206e-01  -0.169    0.866
Stroke_history             2.122e-01  3.180e-01   0.667    0.505
Peripheral.interv         -4.147e-01  3.348e-01  -1.239    0.215
stenose50-70%             -5.289e-01  4.127e+03   0.000    1.000
stenose70-90%             -1.652e+01  3.956e+03  -0.004    0.997
stenose90-99%             -1.702e+01  3.956e+03  -0.004    0.997
stenose100% (Occlusion)   -2.020e-01  4.310e+03   0.000    1.000
LDL_final                 -1.001e-01  1.464e-01  -0.684    0.494

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 343.03  on 339  degrees of freedom
Residual deviance: 322.63  on 321  degrees of freedom
AIC: 360.63

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.172899 
Standard error............: 0.144367 
Odds ratio (effect size)..: 0.841 
Lower 95% CI..............: 0.634 
Upper 95% CI..............: 1.116 
Z-value...................: -1.197636 
P-value...................: 0.231059 
Hosmer and Lemeshow r^2...: 0.059465 
Cox and Snell r^2.........: 0.05823 
Nagelkerke's pseudo r^2...: 0.091646 
Sample size of AE DB......: 2388 
Sample size of model......: 340 
Missing data %............: 85.76214 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite + 
    DiabetesStatus + Stroke_history + LDL_final, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)                 Gendermale  Hypertension.compositeyes     DiabetesStatusDiabetes             Stroke_history  
                  -0.8534                     1.0245                     0.9471                    -0.5168                     0.4840  
                LDL_final  
                   0.2434  

Degrees of Freedom: 340 Total (i.e. Null);  335 Residual
Null Deviance:      343.5 
Residual Deviance: 321.4    AIC: 333.4

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.6383   0.3499   0.5062   0.6887   1.3371  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                10.524691 882.747063   0.012 0.990487    
currentDF[, PROTEIN]        0.154515   0.145906   1.059 0.289599    
Age                        -0.004901   0.018852  -0.260 0.794872    
Gendermale                  1.227524   0.325007   3.777 0.000159 ***
Hypertension.compositeyes   0.930452   0.395899   2.350 0.018762 *  
DiabetesStatusDiabetes     -0.574189   0.363916  -1.578 0.114610    
SmokerCurrentyes            0.281302   0.317408   0.886 0.375485    
Med.Statin.LLDyes          -0.083895   0.371198  -0.226 0.821192    
Med.all.antiplateletyes     0.917466   0.569904   1.610 0.107428    
GFR_MDRD                   -0.007444   0.009202  -0.809 0.418546    
BMI                         0.030642   0.040203   0.762 0.445961    
CAD_history                -0.109329   0.335226  -0.326 0.744322    
Stroke_history              0.427611   0.334719   1.278 0.201418    
Peripheral.interv          -0.220845   0.349112  -0.633 0.527001    
stenose50-70%             -13.821360 882.743866  -0.016 0.987508    
stenose70-90%             -12.101714 882.743616  -0.014 0.989062    
stenose90-99%             -12.351083 882.743599  -0.014 0.988837    
stenose100% (Occlusion)   -11.833540 882.744465  -0.013 0.989304    
LDL_final                   0.273940   0.165892   1.651 0.098674 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 343.48  on 340  degrees of freedom
Residual deviance: 309.36  on 322  degrees of freedom
AIC: 347.36

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.154515 
Standard error............: 0.145906 
Odds ratio (effect size)..: 1.167 
Lower 95% CI..............: 0.877 
Upper 95% CI..............: 1.553 
Z-value...................: 1.059002 
P-value...................: 0.289599 
Hosmer and Lemeshow r^2...: 0.09933 
Cox and Snell r^2.........: 0.09521 
Nagelkerke's pseudo r^2...: 0.149988 
Sample size of AE DB......: 2388 
Sample size of model......: 341 
Missing data %............: 85.72027 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    DiabetesStatus, family = binomial(link = "logit"), data = currentDF)

Coefficients:
           (Intercept)                     Age              Gendermale  DiabetesStatusDiabetes  
              -1.97984                 0.04335                 0.40381                -0.57855  

Degrees of Freedom: 340 Total (i.e. Null);  337 Residual
Null Deviance:      393.6 
Residual Deviance: 377.1    AIC: 385.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1786  -1.1062   0.6502   0.8046   1.3778  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                 8.507942 882.746215   0.010  0.99231   
currentDF[, PROTEIN]        0.053103   0.130729   0.406  0.68459   
Age                         0.044678   0.016690   2.677  0.00743 **
Gendermale                  0.518684   0.290082   1.788  0.07377 . 
Hypertension.compositeyes   0.427483   0.368220   1.161  0.24566   
DiabetesStatusDiabetes     -0.639149   0.323487  -1.976  0.04818 * 
SmokerCurrentyes            0.083062   0.277050   0.300  0.76432   
Med.Statin.LLDyes           0.030957   0.319436   0.097  0.92280   
Med.all.antiplateletyes     0.372978   0.547031   0.682  0.49535   
GFR_MDRD                   -0.002094   0.007832  -0.267  0.78923   
BMI                         0.023163   0.035168   0.659  0.51012   
CAD_history                 0.100163   0.302999   0.331  0.74097   
Stroke_history              0.111451   0.285723   0.390  0.69649   
Peripheral.interv           0.448655   0.342139   1.311  0.18975   
stenose50-70%             -12.121321 882.743894  -0.014  0.98904   
stenose70-90%             -12.421281 882.743569  -0.014  0.98877   
stenose90-99%             -12.359348 882.743564  -0.014  0.98883   
stenose100% (Occlusion)   -11.541968 882.744409  -0.013  0.98957   
LDL_final                   0.114250   0.139299   0.820  0.41212   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 393.60  on 340  degrees of freedom
Residual deviance: 371.24  on 322  degrees of freedom
AIC: 409.24

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.053103 
Standard error............: 0.130729 
Odds ratio (effect size)..: 1.055 
Lower 95% CI..............: 0.816 
Upper 95% CI..............: 1.363 
Z-value...................: 0.406207 
P-value...................: 0.6845907 
Hosmer and Lemeshow r^2...: 0.056802 
Cox and Snell r^2.........: 0.06346 
Nagelkerke's pseudo r^2...: 0.092683 
Sample size of AE DB......: 2388 
Sample size of model......: 341 
Missing data %............: 85.72027 

Analysis of MCP1_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    DiabetesStatus + Peripheral.interv + LDL_final, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes     DiabetesStatusDiabetes          Peripheral.interv                  LDL_final  
                   0.4110                     0.6502                    -0.4656                    -0.4820                    -0.1722  

Degrees of Freedom: 361 Total (i.e. Null);  357 Residual
Null Deviance:      494.9 
Residual Deviance: 482.9    AIC: 492.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8997  -1.1747   0.7992   1.0384   1.7588  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -14.541447 535.414250  -0.027   0.9783  
currentDF[, PROTEIN]       -0.042755   0.110886  -0.386   0.6998  
Age                         0.014087   0.014347   0.982   0.3261  
Gendermale                  0.015604   0.259328   0.060   0.9520  
Hypertension.compositeyes   0.674571   0.325086   2.075   0.0380 *
DiabetesStatusDiabetes     -0.521153   0.295618  -1.763   0.0779 .
SmokerCurrentyes            0.243944   0.239906   1.017   0.3092  
Med.Statin.LLDyes          -0.321147   0.274334  -1.171   0.2417  
Med.all.antiplateletyes     0.693103   0.464849   1.491   0.1360  
GFR_MDRD                   -0.005488   0.006663  -0.824   0.4102  
BMI                         0.017848   0.029365   0.608   0.5433  
CAD_history                 0.019152   0.258874   0.074   0.9410  
Stroke_history             -0.354716   0.242260  -1.464   0.1431  
Peripheral.interv          -0.474133   0.289636  -1.637   0.1016  
stenose50-70%              14.148656 535.411734   0.026   0.9789  
stenose70-90%              13.843373 535.411340   0.026   0.9794  
stenose90-99%              13.476392 535.411334   0.025   0.9799  
stenose100% (Occlusion)    14.975966 535.412896   0.028   0.9777  
LDL_final                  -0.214336   0.117309  -1.827   0.0677 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 494.91  on 361  degrees of freedom
Residual deviance: 470.68  on 343  degrees of freedom
AIC: 508.68

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.042755 
Standard error............: 0.110886 
Odds ratio (effect size)..: 0.958 
Lower 95% CI..............: 0.771 
Upper 95% CI..............: 1.191 
Z-value...................: -0.385574 
P-value...................: 0.6998121 
Hosmer and Lemeshow r^2...: 0.04895 
Cox and Snell r^2.........: 0.064732 
Nagelkerke's pseudo r^2...: 0.086869 
Sample size of AE DB......: 2388 
Sample size of model......: 362 
Missing data %............: 84.84087 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Med.all.antiplatelet + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]  Med.all.antiplateletyes            stenose50-70%            stenose70-90%  
                17.1818                  -0.6076                   0.9495                  -0.2888                 -16.2141  
          stenose90-99%  stenose100% (Occlusion)  
               -16.8072                  -0.1013  

Degrees of Freedom: 360 Total (i.e. Null);  354 Residual
Null Deviance:      366.2 
Residual Deviance: 333.1    AIC: 347.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4374   0.2133   0.5256   0.7029   1.4457  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.747e+01  3.956e+03   0.004   0.9965    
currentDF[, PROTEIN]      -6.062e-01  1.487e-01  -4.078 4.55e-05 ***
Age                        5.814e-03  1.840e-02   0.316   0.7520    
Gendermale                -1.376e-01  3.454e-01  -0.398   0.6904    
Hypertension.compositeyes  3.582e-01  3.944e-01   0.908   0.3637    
DiabetesStatusDiabetes     4.016e-01  4.067e-01   0.987   0.3234    
SmokerCurrentyes           3.919e-01  3.061e-01   1.280   0.2004    
Med.Statin.LLDyes         -4.142e-02  3.399e-01  -0.122   0.9030    
Med.all.antiplateletyes    1.039e+00  5.400e-01   1.924   0.0544 .  
GFR_MDRD                  -6.003e-03  8.531e-03  -0.704   0.4816    
BMI                       -1.599e-02  3.926e-02  -0.407   0.6837    
CAD_history               -5.400e-02  3.195e-01  -0.169   0.8658    
Stroke_history             2.244e-01  3.125e-01   0.718   0.4728    
Peripheral.interv         -3.738e-01  3.532e-01  -1.058   0.2900    
stenose50-70%             -4.501e-01  4.122e+03   0.000   0.9999    
stenose70-90%             -1.648e+01  3.956e+03  -0.004   0.9967    
stenose90-99%             -1.712e+01  3.956e+03  -0.004   0.9965    
stenose100% (Occlusion)   -2.379e-01  4.336e+03   0.000   1.0000    
LDL_final                  4.524e-03  1.476e-01   0.031   0.9755    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 366.23  on 360  degrees of freedom
Residual deviance: 326.43  on 342  degrees of freedom
AIC: 364.43

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.606247 
Standard error............: 0.148674 
Odds ratio (effect size)..: 0.545 
Lower 95% CI..............: 0.408 
Upper 95% CI..............: 0.73 
Z-value...................: -4.077683 
P-value...................: 4.548676e-05 
Hosmer and Lemeshow r^2...: 0.108674 
Cox and Snell r^2.........: 0.104387 
Nagelkerke's pseudo r^2...: 0.163768 
Sample size of AE DB......: 2388 
Sample size of model......: 361 
Missing data %............: 84.88275 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Hypertension.composite + SmokerCurrent + stenose + 
    LDL_final, family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]                 Gendermale  Hypertension.compositeyes           SmokerCurrentyes  
                  11.7736                     0.5101                     0.9142                     0.9542                     0.4421  
            stenose50-70%              stenose70-90%              stenose90-99%    stenose100% (Occlusion)                  LDL_final  
                 -14.0712                   -12.1842                   -12.7510                   -12.4564                     0.2372  

Degrees of Freedom: 361 Total (i.e. Null);  352 Residual
Null Deviance:      361.2 
Residual Deviance: 321.1    AIC: 341.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3842   0.2960   0.4886   0.6684   1.6498  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                10.395966 882.746608   0.012  0.99060    
currentDF[, PROTEIN]        0.498113   0.150588   3.308  0.00094 ***
Age                         0.002141   0.018255   0.117  0.90662    
Gendermale                  0.936906   0.314287   2.981  0.00287 ** 
Hypertension.compositeyes   0.962021   0.389396   2.471  0.01349 *  
DiabetesStatusDiabetes     -0.217385   0.369876  -0.588  0.55672    
SmokerCurrentyes            0.421196   0.313609   1.343  0.17925    
Med.Statin.LLDyes          -0.038865   0.357900  -0.109  0.91353    
Med.all.antiplateletyes     0.483579   0.553916   0.873  0.38265    
GFR_MDRD                   -0.003421   0.008894  -0.385  0.70053    
BMI                         0.026465   0.037523   0.705  0.48062    
CAD_history                -0.069242   0.335113  -0.207  0.83631    
Stroke_history              0.332254   0.328273   1.012  0.31148    
Peripheral.interv          -0.104067   0.358243  -0.290  0.77144    
stenose50-70%             -13.706793 882.743836  -0.016  0.98761    
stenose70-90%             -11.838995 882.743573  -0.013  0.98930    
stenose90-99%             -12.435399 882.743559  -0.014  0.98876    
stenose100% (Occlusion)   -11.892994 882.744422  -0.013  0.98925    
LDL_final                   0.237520   0.160466   1.480  0.13882    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 361.18  on 361  degrees of freedom
Residual deviance: 317.86  on 343  degrees of freedom
AIC: 355.86

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.498113 
Standard error............: 0.150588 
Odds ratio (effect size)..: 1.646 
Lower 95% CI..............: 1.225 
Upper 95% CI..............: 2.211 
Z-value...................: 3.30779 
P-value...................: 0.0009403531 
Hosmer and Lemeshow r^2...: 0.119926 
Cox and Snell r^2.........: 0.112773 
Nagelkerke's pseudo r^2...: 0.17864 
Sample size of AE DB......: 2388 
Sample size of model......: 362 
Missing data %............: 84.84087 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    DiabetesStatus + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
           (Intercept)                     Age              Gendermale  DiabetesStatusDiabetes       Peripheral.interv  
              -1.38377                 0.03137                 0.57820                -0.59140                 0.47188  

Degrees of Freedom: 361 Total (i.e. Null);  357 Residual
Null Deviance:      414.6 
Residual Deviance: 398.2    AIC: 408.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1230  -1.1154   0.6446   0.8048   1.3790  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                 9.817110 882.745818   0.011   0.9911  
currentDF[, PROTEIN]        0.021154   0.126653   0.167   0.8673  
Age                         0.029966   0.016143   1.856   0.0634 .
Gendermale                  0.615473   0.280452   2.195   0.0282 *
Hypertension.compositeyes   0.351264   0.354391   0.991   0.3216  
DiabetesStatusDiabetes     -0.578653   0.312855  -1.850   0.0644 .
SmokerCurrentyes            0.118988   0.270115   0.441   0.6596  
Med.Statin.LLDyes          -0.164474   0.314860  -0.522   0.6014  
Med.all.antiplateletyes     0.451435   0.498323   0.906   0.3650  
GFR_MDRD                   -0.001996   0.007621  -0.262   0.7934  
BMI                         0.019887   0.032441   0.613   0.5399  
CAD_history                 0.239472   0.302172   0.793   0.4281  
Stroke_history              0.199976   0.278586   0.718   0.4729  
Peripheral.interv           0.451404   0.347558   1.299   0.1940  
stenose50-70%             -12.013293 882.743893  -0.014   0.9891  
stenose70-90%             -12.605523 882.743522  -0.014   0.9886  
stenose90-99%             -12.575565 882.743518  -0.014   0.9886  
stenose100% (Occlusion)   -12.140411 882.744417  -0.014   0.9890  
LDL_final                   0.091522   0.135650   0.675   0.4999  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 414.64  on 361  degrees of freedom
Residual deviance: 392.62  on 343  degrees of freedom
AIC: 430.62

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.021154 
Standard error............: 0.126653 
Odds ratio (effect size)..: 1.021 
Lower 95% CI..............: 0.797 
Upper 95% CI..............: 1.309 
Z-value...................: 0.167027 
P-value...................: 0.867349 
Hosmer and Lemeshow r^2...: 0.053111 
Cox and Snell r^2.........: 0.059021 
Nagelkerke's pseudo r^2...: 0.086553 
Sample size of AE DB......: 2388 
Sample size of model......: 362 
Missing data %............: 84.84087 

Analysis of IL6_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent + 
    LDL_final, family = binomial(link = "logit"), data = currentDF)

Coefficients:
     (Intercept)               Age  SmokerCurrentyes         LDL_final  
        -1.27196           0.02486           0.41455          -0.16393  

Degrees of Freedom: 619 Total (i.e. Null);  616 Residual
Null Deviance:      857.9 
Residual Deviance: 843  AIC: 851

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7301  -1.1761   0.8192   1.0957   1.7649  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                -1.747181   1.735445  -1.007  0.31405   
currentDF[, PROTEIN]       -0.035015   0.084708  -0.413  0.67934   
Age                         0.029151   0.010296   2.831  0.00464 **
Gendermale                  0.053398   0.184699   0.289  0.77250   
Hypertension.compositeyes   0.190931   0.256757   0.744  0.45710   
DiabetesStatusDiabetes     -0.117544   0.208989  -0.562  0.57381   
SmokerCurrentyes            0.473160   0.183544   2.578  0.00994 **
Med.Statin.LLDyes          -0.273095   0.211314  -1.292  0.19623   
Med.all.antiplateletyes     0.134078   0.293577   0.457  0.64788   
GFR_MDRD                    0.005082   0.004527   1.123  0.26159   
BMI                         0.018661   0.022444   0.831  0.40570   
CAD_history                 0.018320   0.193210   0.095  0.92446   
Stroke_history             -0.225916   0.181521  -1.245  0.21329   
Peripheral.interv          -0.330372   0.219549  -1.505  0.13238   
stenose50-70%              -1.299569   1.303726  -0.997  0.31886   
stenose70-90%              -0.544255   1.245214  -0.437  0.66205   
stenose90-99%              -0.447456   1.243621  -0.360  0.71900   
stenose100% (Occlusion)     0.159048   1.512113   0.105  0.91623   
stenose70-99%             -15.712343 605.394374  -0.026  0.97929   
LDL_final                  -0.209060   0.086575  -2.415  0.01574 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 857.85  on 619  degrees of freedom
Residual deviance: 824.90  on 600  degrees of freedom
AIC: 864.9

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.035015 
Standard error............: 0.084708 
Odds ratio (effect size)..: 0.966 
Lower 95% CI..............: 0.818 
Upper 95% CI..............: 1.14 
Z-value...................: -0.413367 
P-value...................: 0.679338 
Hosmer and Lemeshow r^2...: 0.03841 
Cox and Snell r^2.........: 0.051757 
Nagelkerke's pseudo r^2...: 0.069071 
Sample size of AE DB......: 2388 
Sample size of model......: 620 
Missing data %............: 74.03685 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerCurrent, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]      SmokerCurrentyes  
              1.1691               -0.2488                0.5069  

Degrees of Freedom: 621 Total (i.e. Null);  619 Residual
Null Deviance:      643 
Residual Deviance: 631.9    AIC: 637.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2145   0.3945   0.6231   0.7404   1.0366  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.397e+01  1.384e+03   0.010   0.9920  
currentDF[, PROTEIN]      -2.191e-01  1.036e-01  -2.115   0.0344 *
Age                        3.352e-03  1.245e-02   0.269   0.7878  
Gendermale                -9.782e-02  2.254e-01  -0.434   0.6643  
Hypertension.compositeyes  7.913e-02  3.052e-01   0.259   0.7954  
DiabetesStatusDiabetes     2.197e-01  2.604e-01   0.844   0.3987  
SmokerCurrentyes           5.167e-01  2.289e-01   2.257   0.0240 *
Med.Statin.LLDyes          1.046e-01  2.524e-01   0.414   0.6787  
Med.all.antiplateletyes    6.134e-02  3.509e-01   0.175   0.8612  
GFR_MDRD                   5.423e-03  5.436e-03   0.998   0.3185  
BMI                        3.696e-02  2.912e-02   1.269   0.2045  
CAD_history                2.907e-01  2.389e-01   1.217   0.2236  
Stroke_history             1.541e-01  2.218e-01   0.695   0.4871  
Peripheral.interv          1.196e-01  2.760e-01   0.433   0.6649  
stenose50-70%             -1.425e+01  1.384e+03  -0.010   0.9918  
stenose70-90%             -1.513e+01  1.384e+03  -0.011   0.9913  
stenose90-99%             -1.512e+01  1.384e+03  -0.011   0.9913  
stenose100% (Occlusion)    3.704e-01  1.647e+03   0.000   0.9998  
stenose70-99%              3.154e-01  2.190e+03   0.000   0.9999  
LDL_final                  1.246e-01  1.046e-01   1.192   0.2332  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 643.0  on 621  degrees of freedom
Residual deviance: 617.3  on 602  degrees of freedom
AIC: 657.3

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.219079 
Standard error............: 0.103595 
Odds ratio (effect size)..: 0.803 
Lower 95% CI..............: 0.656 
Upper 95% CI..............: 0.984 
Z-value...................: -2.114772 
P-value...................: 0.03444938 
Hosmer and Lemeshow r^2...: 0.039977 
Cox and Snell r^2.........: 0.040484 
Nagelkerke's pseudo r^2...: 0.062831 
Sample size of AE DB......: 2388 
Sample size of model......: 622 
Missing data %............: 73.9531 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv + LDL_final, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale        Stroke_history     Peripheral.interv             LDL_final  
             -0.0551                0.4691                0.9566                0.3535               -0.5271                0.1711  

Degrees of Freedom: 621 Total (i.e. Null);  616 Residual
Null Deviance:      717.6 
Residual Deviance: 660  AIC: 672

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3823  -0.9479   0.5924   0.7833   1.4736  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                12.858469 499.149839   0.026   0.9794    
currentDF[, PROTEIN]        0.467195   0.101826   4.588 4.47e-06 ***
Age                         0.003838   0.011769   0.326   0.7444    
Gendermale                  0.981790   0.208027   4.720 2.36e-06 ***
Hypertension.compositeyes   0.165307   0.297749   0.555   0.5788    
DiabetesStatusDiabetes     -0.102798   0.239740  -0.429   0.6681    
SmokerCurrentyes            0.152551   0.213444   0.715   0.4748    
Med.Statin.LLDyes          -0.279226   0.256107  -1.090   0.2756    
Med.all.antiplateletyes     0.210611   0.337766   0.624   0.5329    
GFR_MDRD                   -0.004117   0.005317  -0.774   0.4388    
BMI                         0.014587   0.025497   0.572   0.5672    
CAD_history                 0.150945   0.226514   0.666   0.5052    
Stroke_history              0.353781   0.221467   1.597   0.1102    
Peripheral.interv          -0.579450   0.239612  -2.418   0.0156 *  
stenose50-70%             -13.716620 499.148035  -0.027   0.9781    
stenose70-90%             -13.360306 499.147872  -0.027   0.9786    
stenose90-99%             -13.401952 499.147867  -0.027   0.9786    
stenose100% (Occlusion)   -14.231873 499.148478  -0.029   0.9773    
stenose70-99%             -14.409382 499.149988  -0.029   0.9770    
LDL_final                   0.152308   0.101184   1.505   0.1323    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 717.61  on 621  degrees of freedom
Residual deviance: 651.64  on 602  degrees of freedom
AIC: 691.64

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.467195 
Standard error............: 0.101826 
Odds ratio (effect size)..: 1.596 
Lower 95% CI..............: 1.307 
Upper 95% CI..............: 1.948 
Z-value...................: 4.588153 
P-value...................: 4.471852e-06 
Hosmer and Lemeshow r^2...: 0.091923 
Cox and Snell r^2.........: 0.100623 
Nagelkerke's pseudo r^2...: 0.146995 
Sample size of AE DB......: 2388 
Sample size of model......: 622 
Missing data %............: 73.9531 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + CAD_history + 
    Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)                      Age               Gendermale         SmokerCurrentyes        Med.Statin.LLDyes  
               -1.75978                  0.01881                  0.57921                  0.34166                 -0.34769  
Med.all.antiplateletyes              CAD_history           Stroke_history        Peripheral.interv  
                0.61066                  0.29144                  0.30926                  0.34737  

Degrees of Freedom: 620 Total (i.e. Null);  612 Residual
Null Deviance:      814.3 
Residual Deviance: 786.5    AIC: 804.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0242  -1.2532   0.7743   0.9517   1.3858  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)   
(Intercept)               -2.479246   1.769091  -1.401   0.1611   
currentDF[, PROTEIN]       0.073565   0.088029   0.836   0.4033   
Age                        0.019453   0.010570   1.840   0.0657 . 
Gendermale                 0.616088   0.188818   3.263   0.0011 **
Hypertension.compositeyes  0.100506   0.262263   0.383   0.7016   
DiabetesStatusDiabetes    -0.108476   0.214973  -0.505   0.6138   
SmokerCurrentyes           0.334411   0.190624   1.754   0.0794 . 
Med.Statin.LLDyes         -0.276525   0.223388  -1.238   0.2158   
Med.all.antiplateletyes    0.602682   0.296079   2.036   0.0418 * 
GFR_MDRD                  -0.002012   0.004711  -0.427   0.6693   
BMI                        0.013881   0.023105   0.601   0.5480   
CAD_history                0.290876   0.203374   1.430   0.1526   
Stroke_history             0.315478   0.192242   1.641   0.1008   
Peripheral.interv          0.335048   0.234753   1.427   0.1535   
stenose50-70%             -0.140654   1.303328  -0.108   0.9141   
stenose70-90%              0.056986   1.248890   0.046   0.9636   
stenose90-99%              0.165129   1.247312   0.132   0.8947   
stenose100% (Occlusion)    0.008095   1.474009   0.005   0.9956   
stenose70-99%             -0.823543   1.907537  -0.432   0.6659   
LDL_final                  0.078241   0.088311   0.886   0.3756   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 814.31  on 620  degrees of freedom
Residual deviance: 782.57  on 601  degrees of freedom
AIC: 822.57

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.073565 
Standard error............: 0.088029 
Odds ratio (effect size)..: 1.076 
Lower 95% CI..............: 0.906 
Upper 95% CI..............: 1.279 
Z-value...................: 0.835693 
P-value...................: 0.4033275 
Hosmer and Lemeshow r^2...: 0.038982 
Cox and Snell r^2.........: 0.049832 
Nagelkerke's pseudo r^2...: 0.068213 
Sample size of AE DB......: 2388 
Sample size of model......: 621 
Missing data %............: 73.99497 

Analysis of IL6R_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent + 
    Peripheral.interv + stenose + LDL_final, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)                      Age         SmokerCurrentyes        Peripheral.interv            stenose50-70%  
                13.3099                   0.0214                   0.3256                  -0.3539                 -15.0020  
          stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose70-99%                LDL_final  
               -14.3302                 -14.2143                 -13.5827                 -29.4019                  -0.1460  

Degrees of Freedom: 613 Total (i.e. Null);  604 Residual
Null Deviance:      849.3 
Residual Deviance: 826.6    AIC: 846.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6687  -1.1942   0.8591   1.1070   1.6325  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                12.728624 623.837081   0.020   0.9837  
currentDF[, PROTEIN]       -0.100966   0.086498  -1.167   0.2431  
Age                         0.023322   0.010485   2.224   0.0261 *
Gendermale                  0.008490   0.184910   0.046   0.9634  
Hypertension.compositeyes   0.227501   0.252033   0.903   0.3667  
DiabetesStatusDiabetes     -0.170953   0.211950  -0.807   0.4199  
SmokerCurrentyes            0.358610   0.183428   1.955   0.0506 .
Med.Statin.LLDyes          -0.206499   0.214384  -0.963   0.3354  
Med.all.antiplateletyes     0.124408   0.297736   0.418   0.6761  
GFR_MDRD                    0.003341   0.004657   0.717   0.4731  
BMI                         0.008807   0.023091   0.381   0.7029  
CAD_history                 0.004566   0.193360   0.024   0.9812  
Stroke_history             -0.185775   0.181611  -1.023   0.3063  
Peripheral.interv          -0.354190   0.221874  -1.596   0.1104  
stenose50-70%             -15.051615 623.836044  -0.024   0.9808  
stenose70-90%             -14.308766 623.835926  -0.023   0.9817  
stenose90-99%             -14.199931 623.835923  -0.023   0.9818  
stenose100% (Occlusion)   -13.640920 623.836514  -0.022   0.9826  
stenose70-99%             -29.520435 870.648387  -0.034   0.9730  
LDL_final                  -0.179057   0.087640  -2.043   0.0410 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 849.30  on 613  degrees of freedom
Residual deviance: 820.65  on 594  degrees of freedom
AIC: 860.65

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.100966 
Standard error............: 0.086498 
Odds ratio (effect size)..: 0.904 
Lower 95% CI..............: 0.763 
Upper 95% CI..............: 1.071 
Z-value...................: -1.167258 
P-value...................: 0.243106 
Hosmer and Lemeshow r^2...: 0.033731 
Cox and Snell r^2.........: 0.045586 
Nagelkerke's pseudo r^2...: 0.060843 
Sample size of AE DB......: 2388 
Sample size of model......: 614 
Missing data %............: 74.28811 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
     (Intercept)  SmokerCurrentyes  
          1.1229            0.4918  

Degrees of Freedom: 615 Total (i.e. Null);  614 Residual
Null Deviance:      642.7 
Residual Deviance: 637.2    AIC: 641.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1875   0.4255   0.6428   0.7528   1.0398  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.357e+01  1.696e+03   0.008   0.9936  
currentDF[, PROTEIN]       4.242e-02  1.036e-01   0.410   0.6821  
Age                        5.963e-03  1.253e-02   0.476   0.6342  
Gendermale                -2.572e-03  2.218e-01  -0.012   0.9907  
Hypertension.compositeyes -9.392e-04  3.017e-01  -0.003   0.9975  
DiabetesStatusDiabetes     3.018e-01  2.631e-01   1.147   0.2513  
SmokerCurrentyes           5.109e-01  2.287e-01   2.234   0.0255 *
Med.Statin.LLDyes          1.612e-01  2.533e-01   0.637   0.5244  
Med.all.antiplateletyes    2.178e-01  3.457e-01   0.630   0.5287  
GFR_MDRD                   3.951e-03  5.534e-03   0.714   0.4753  
BMI                        3.821e-02  2.896e-02   1.319   0.1870  
CAD_history                2.479e-01  2.368e-01   1.047   0.2951  
Stroke_history             8.299e-02  2.202e-01   0.377   0.7063  
Peripheral.interv          1.318e-01  2.762e-01   0.477   0.6331  
stenose50-70%             -1.413e+01  1.696e+03  -0.008   0.9934  
stenose70-90%             -1.508e+01  1.696e+03  -0.009   0.9929  
stenose90-99%             -1.498e+01  1.696e+03  -0.009   0.9930  
stenose100% (Occlusion)    5.714e-01  1.918e+03   0.000   0.9998  
stenose70-99%              6.645e-01  2.399e+03   0.000   0.9998  
LDL_final                  1.168e-01  1.055e-01   1.107   0.2683  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 642.71  on 615  degrees of freedom
Residual deviance: 622.38  on 596  degrees of freedom
AIC: 662.38

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.042418 
Standard error............: 0.103555 
Odds ratio (effect size)..: 1.043 
Lower 95% CI..............: 0.852 
Upper 95% CI..............: 1.278 
Z-value...................: 0.409621 
P-value...................: 0.6820839 
Hosmer and Lemeshow r^2...: 0.031636 
Cox and Snell r^2.........: 0.032469 
Nagelkerke's pseudo r^2...: 0.050127 
Sample size of AE DB......: 2388 
Sample size of model......: 616 
Missing data %............: 74.20436 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Stroke_history + 
    Peripheral.interv + LDL_final, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
      (Intercept)         Gendermale     Stroke_history  Peripheral.interv          LDL_final  
          -0.1296             0.9069             0.4893            -0.5359             0.1945  

Degrees of Freedom: 615 Total (i.e. Null);  611 Residual
Null Deviance:      705.7 
Residual Deviance: 669.9    AIC: 679.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2447  -1.0798   0.6262   0.7779   1.3515  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                13.310670 623.678659   0.021   0.9830    
currentDF[, PROTEIN]        0.034199   0.099179   0.345   0.7302    
Age                         0.002162   0.012062   0.179   0.8578    
Gendermale                  0.945942   0.205147   4.611 4.01e-06 ***
Hypertension.compositeyes   0.185795   0.287996   0.645   0.5188    
DiabetesStatusDiabetes     -0.050143   0.241878  -0.207   0.8358    
SmokerCurrentyes            0.200981   0.211717   0.949   0.3425    
Med.Statin.LLDyes          -0.259883   0.259751  -1.001   0.3171    
Med.all.antiplateletyes     0.190832   0.342756   0.557   0.5777    
GFR_MDRD                   -0.002988   0.005464  -0.547   0.5845    
BMI                        -0.009114   0.026181  -0.348   0.7278    
CAD_history                 0.043912   0.222949   0.197   0.8439    
Stroke_history              0.471988   0.220383   2.142   0.0322 *  
Peripheral.interv          -0.587090   0.239216  -2.454   0.0141 *  
stenose50-70%             -13.713838 623.677282  -0.022   0.9825    
stenose70-90%             -13.172205 623.677152  -0.021   0.9831    
stenose90-99%             -13.285582 623.677146  -0.021   0.9830    
stenose100% (Occlusion)   -14.098075 623.677650  -0.023   0.9820    
stenose70-99%             -14.625391 623.678836  -0.023   0.9813    
LDL_final                   0.163890   0.102288   1.602   0.1091    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 705.67  on 615  degrees of freedom
Residual deviance: 662.35  on 596  degrees of freedom
AIC: 702.35

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.034199 
Standard error............: 0.099179 
Odds ratio (effect size)..: 1.035 
Lower 95% CI..............: 0.852 
Upper 95% CI..............: 1.257 
Z-value...................: 0.344821 
P-value...................: 0.7302291 
Hosmer and Lemeshow r^2...: 0.061387 
Cox and Snell r^2.........: 0.067907 
Nagelkerke's pseudo r^2...: 0.099577 
Sample size of AE DB......: 2388 
Sample size of model......: 616 
Missing data %............: 74.20436 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Med.all.antiplatelet + Stroke_history + Peripheral.interv + 
    LDL_final, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age               Gendermale  Med.all.antiplateletyes  
               -2.07785                  0.13314                  0.01767                  0.65321                  0.48369  
         Stroke_history        Peripheral.interv                LDL_final  
                0.29070                  0.40758                  0.12410  

Degrees of Freedom: 614 Total (i.e. Null);  607 Residual
Null Deviance:      809.9 
Residual Deviance: 785.9    AIC: 801.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0019  -1.2521   0.7763   0.9615   1.3905  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -2.1641077  1.9053663  -1.136 0.256042    
currentDF[, PROTEIN]       0.1088200  0.0895386   1.215 0.224236    
Age                        0.0163295  0.0108111   1.510 0.130932    
Gendermale                 0.6517166  0.1888045   3.452 0.000557 ***
Hypertension.compositeyes  0.1030330  0.2576892   0.400 0.689279    
DiabetesStatusDiabetes    -0.1919952  0.2164323  -0.887 0.375030    
SmokerCurrentyes           0.2857827  0.1913494   1.494 0.135303    
Med.Statin.LLDyes         -0.2228589  0.2268089  -0.983 0.325812    
Med.all.antiplateletyes    0.5088318  0.3038315   1.675 0.093990 .  
GFR_MDRD                  -0.0037458  0.0048722  -0.769 0.442005    
BMI                       -0.0006517  0.0236286  -0.028 0.977995    
CAD_history                0.2608588  0.2031769   1.284 0.199177    
Stroke_history             0.3035600  0.1922447   1.579 0.114329    
Peripheral.interv          0.3490620  0.2385831   1.463 0.143450    
stenose50-70%              0.3834730  1.4877947   0.258 0.796603    
stenose70-90%              0.4822793  1.4379089   0.335 0.737321    
stenose90-99%              0.5349906  1.4365213   0.372 0.709579    
stenose100% (Occlusion)    0.4214479  1.6385851   0.257 0.797023    
stenose70-99%             -0.3476959  2.0349403  -0.171 0.864332    
LDL_final                  0.0870989  0.0901723   0.966 0.334086    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 809.93  on 614  degrees of freedom
Residual deviance: 779.36  on 595  degrees of freedom
AIC: 819.36

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.10882 
Standard error............: 0.089539 
Odds ratio (effect size)..: 1.115 
Lower 95% CI..............: 0.935 
Upper 95% CI..............: 1.329 
Z-value...................: 1.215341 
P-value...................: 0.2242361 
Hosmer and Lemeshow r^2...: 0.037739 
Cox and Snell r^2.........: 0.048486 
Nagelkerke's pseudo r^2...: 0.066233 
Sample size of AE DB......: 2388 
Sample size of model......: 615 
Missing data %............: 74.24623 

Analysis of MCP1_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + SmokerCurrent + Peripheral.interv + LDL_final, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age      SmokerCurrentyes     Peripheral.interv             LDL_final  
            -1.34079              -0.39792               0.02462               0.40432              -0.30868              -0.11552  

Degrees of Freedom: 635 Total (i.e. Null);  630 Residual
Null Deviance:      880.3 
Residual Deviance: 840  AIC: 852

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8218  -1.1434   0.7217   1.0746   1.8206  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                -1.817023   1.746256  -1.041  0.29810    
currentDF[, PROTEIN]       -0.399085   0.084476  -4.724 2.31e-06 ***
Age                         0.028293   0.010412   2.717  0.00658 ** 
Gendermale                  0.078341   0.185488   0.422  0.67277    
Hypertension.compositeyes   0.161837   0.253933   0.637  0.52392    
DiabetesStatusDiabetes     -0.110912   0.210609  -0.527  0.59845    
SmokerCurrentyes            0.436300   0.184027   2.371  0.01775 *  
Med.Statin.LLDyes          -0.262370   0.212952  -1.232  0.21793    
Med.all.antiplateletyes     0.181594   0.292060   0.622  0.53409    
GFR_MDRD                    0.003821   0.004546   0.840  0.40070    
BMI                         0.013442   0.022487   0.598  0.55000    
CAD_history                 0.039467   0.193046   0.204  0.83801    
Stroke_history             -0.130531   0.182804  -0.714  0.47520    
Peripheral.interv          -0.358151   0.221203  -1.619  0.10542    
stenose50-70%              -1.278641   1.309167  -0.977  0.32873    
stenose70-90%              -0.382827   1.248198  -0.307  0.75907    
stenose90-99%              -0.361105   1.246825  -0.290  0.77211    
stenose100% (Occlusion)     0.094999   1.515418   0.063  0.95001    
stenose70-99%             -15.461099 595.304646  -0.026  0.97928    
LDL_final                  -0.149867   0.087204  -1.719  0.08569 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 880.27  on 635  degrees of freedom
Residual deviance: 826.32  on 616  degrees of freedom
AIC: 866.32

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.399085 
Standard error............: 0.084476 
Odds ratio (effect size)..: 0.671 
Lower 95% CI..............: 0.569 
Upper 95% CI..............: 0.792 
Z-value...................: -4.724228 
P-value...................: 2.309916e-06 
Hosmer and Lemeshow r^2...: 0.061283 
Cox and Snell r^2.........: 0.081322 
Nagelkerke's pseudo r^2...: 0.108509 
Sample size of AE DB......: 2388 
Sample size of model......: 636 
Missing data %............: 73.36683 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerCurrent, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]      SmokerCurrentyes  
              1.1665               -0.2621                0.5075  

Degrees of Freedom: 637 Total (i.e. Null);  635 Residual
Null Deviance:      658.5 
Residual Deviance: 644.7    AIC: 650.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2941   0.3843   0.6168   0.7386   1.1162  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.370e+01  1.384e+03   0.010  0.99210   
currentDF[, PROTEIN]      -2.590e-01  9.751e-02  -2.657  0.00789 **
Age                        4.463e-03  1.241e-02   0.360  0.71918   
Gendermale                -1.234e-02  2.222e-01  -0.056  0.95570   
Hypertension.compositeyes -4.807e-02  3.030e-01  -0.159  0.87395   
DiabetesStatusDiabetes     2.668e-01  2.602e-01   1.025  0.30517   
SmokerCurrentyes           5.095e-01  2.285e-01   2.230  0.02577 * 
Med.Statin.LLDyes          1.431e-01  2.509e-01   0.570  0.56847   
Med.all.antiplateletyes    8.843e-02  3.421e-01   0.259  0.79601   
GFR_MDRD                   4.884e-03  5.411e-03   0.903  0.36673   
BMI                        4.008e-02  2.882e-02   1.391  0.16431   
CAD_history                2.587e-01  2.349e-01   1.101  0.27074   
Stroke_history             1.059e-01  2.196e-01   0.482  0.62957   
Peripheral.interv          1.538e-01  2.756e-01   0.558  0.57682   
stenose50-70%             -1.421e+01  1.384e+03  -0.010  0.99181   
stenose70-90%             -1.508e+01  1.384e+03  -0.011  0.99130   
stenose90-99%             -1.502e+01  1.384e+03  -0.011  0.99134   
stenose100% (Occlusion)    2.773e-01  1.651e+03   0.000  0.99987   
stenose70-99%              6.816e-01  2.189e+03   0.000  0.99975   
LDL_final                  1.548e-01  1.051e-01   1.473  0.14083   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 658.50  on 637  degrees of freedom
Residual deviance: 629.52  on 618  degrees of freedom
AIC: 669.52

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.259041 
Standard error............: 0.097506 
Odds ratio (effect size)..: 0.772 
Lower 95% CI..............: 0.638 
Upper 95% CI..............: 0.934 
Z-value...................: -2.656673 
P-value...................: 0.007891602 
Hosmer and Lemeshow r^2...: 0.044018 
Cox and Snell r^2.........: 0.044416 
Nagelkerke's pseudo r^2...: 0.068995 
Sample size of AE DB......: 2388 
Sample size of model......: 638 
Missing data %............: 73.28308 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Stroke_history + 
    Peripheral.interv + LDL_final, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
      (Intercept)         Gendermale     Stroke_history  Peripheral.interv          LDL_final  
          -0.1173             0.9603             0.4530            -0.4809             0.1684  

Degrees of Freedom: 637 Total (i.e. Null);  633 Residual
Null Deviance:      737.7 
Residual Deviance: 700.3    AIC: 710.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2911  -1.0945   0.6282   0.7934   1.3752  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.338e+01  5.031e+02   0.027   0.9788    
currentDF[, PROTEIN]       1.202e-01  9.253e-02   1.299   0.1940    
Age                        1.348e-03  1.157e-02   0.117   0.9072    
Gendermale                 9.977e-01  2.005e-01   4.976  6.5e-07 ***
Hypertension.compositeyes  2.073e-01  2.844e-01   0.729   0.4660    
DiabetesStatusDiabetes    -1.147e-01  2.332e-01  -0.492   0.6227    
SmokerCurrentyes           2.294e-01  2.070e-01   1.108   0.2678    
Med.Statin.LLDyes         -2.872e-01  2.521e-01  -1.139   0.2546    
Med.all.antiplateletyes    2.759e-01  3.253e-01   0.848   0.3965    
GFR_MDRD                  -5.717e-03  5.177e-03  -1.104   0.2695    
BMI                        8.212e-04  2.472e-02   0.033   0.9735    
CAD_history                4.738e-02  2.173e-01   0.218   0.8274    
Stroke_history             4.034e-01  2.162e-01   1.866   0.0620 .  
Peripheral.interv         -5.442e-01  2.343e-01  -2.323   0.0202 *  
stenose50-70%             -1.370e+01  5.031e+02  -0.027   0.9783    
stenose70-90%             -1.326e+01  5.031e+02  -0.026   0.9790    
stenose90-99%             -1.340e+01  5.031e+02  -0.027   0.9788    
stenose100% (Occlusion)   -1.410e+01  5.031e+02  -0.028   0.9776    
stenose70-99%             -1.479e+01  5.031e+02  -0.029   0.9765    
LDL_final                  1.304e-01  9.848e-02   1.324   0.1856    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 737.67  on 637  degrees of freedom
Residual deviance: 688.83  on 618  degrees of freedom
AIC: 728.83

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.120194 
Standard error............: 0.092531 
Odds ratio (effect size)..: 1.128 
Lower 95% CI..............: 0.941 
Upper 95% CI..............: 1.352 
Z-value...................: 1.298962 
P-value...................: 0.193957 
Hosmer and Lemeshow r^2...: 0.0662 
Cox and Snell r^2.........: 0.073686 
Nagelkerke's pseudo r^2...: 0.10752 
Sample size of AE DB......: 2388 
Sample size of model......: 638 
Missing data %............: 73.28308 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + 
    CAD_history + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age               Gendermale         SmokerCurrentyes  
               -1.67915                 -0.12054                  0.01775                  0.61982                  0.34171  
      Med.Statin.LLDyes  Med.all.antiplateletyes              CAD_history           Stroke_history        Peripheral.interv  
               -0.43459                  0.59803                  0.30304                  0.37355                  0.40386  

Degrees of Freedom: 636 Total (i.e. Null);  627 Residual
Null Deviance:      837.7 
Residual Deviance: 804.1    AIC: 824.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9427  -1.2478   0.7625   0.9458   1.5222  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -2.127639   1.772751  -1.200  0.23007    
currentDF[, PROTEIN]      -0.131427   0.083463  -1.575  0.11533    
Age                        0.017852   0.010561   1.690  0.09095 .  
Gendermale                 0.657838   0.186851   3.521  0.00043 ***
Hypertension.compositeyes  0.037915   0.257418   0.147  0.88290    
DiabetesStatusDiabetes    -0.150861   0.212631  -0.709  0.47802    
SmokerCurrentyes           0.335587   0.188712   1.778  0.07535 .  
Med.Statin.LLDyes         -0.375382   0.224150  -1.675  0.09399 .  
Med.all.antiplateletyes    0.592618   0.292041   2.029  0.04244 *  
GFR_MDRD                  -0.002568   0.004689  -0.548  0.58388    
BMI                        0.007938   0.022724   0.349  0.72684    
CAD_history                0.304739   0.199481   1.528  0.12660    
Stroke_history             0.391671   0.191529   2.045  0.04086 *  
Peripheral.interv          0.399298   0.234514   1.703  0.08863 .  
stenose50-70%             -0.123512   1.318453  -0.094  0.92536    
stenose70-90%              0.121608   1.264382   0.096  0.92338    
stenose90-99%              0.190347   1.262909   0.151  0.88020    
stenose100% (Occlusion)   -0.048944   1.488937  -0.033  0.97378    
stenose70-99%             -0.908097   1.915050  -0.474  0.63536    
LDL_final                  0.073318   0.088201   0.831  0.40583    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 837.69  on 636  degrees of freedom
Residual deviance: 801.03  on 617  degrees of freedom
AIC: 841.03

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: -0.131427 
Standard error............: 0.083463 
Odds ratio (effect size)..: 0.877 
Lower 95% CI..............: 0.745 
Upper 95% CI..............: 1.033 
Z-value...................: -1.574674 
P-value...................: 0.1153318 
Hosmer and Lemeshow r^2...: 0.043766 
Cox and Snell r^2.........: 0.05593 
Nagelkerke's pseudo r^2...: 0.076455 
Sample size of AE DB......: 2388 
Sample size of model......: 637 
Missing data %............: 73.32496 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 4

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, stenosis, and hsCRP.

Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_LN.

- processing Macrophages_LN
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(COVARIATES_M4)` instead of `COVARIATES_M4` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.

Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD + 
    CAD_history + hsCRP_plasma, data = currentDF)

Coefficients:
           (Intercept)  DiabetesStatusDiabetes                GFR_MDRD             CAD_history            hsCRP_plasma  
              3.332070               -0.355918                0.008097                0.225954                0.002694  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8586 -0.7513 -0.0263  0.6686  3.7591 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                7.154970   1.598166   4.477 1.27e-05 ***
currentDF[, TRAIT]        -0.042782   0.036352  -1.177   0.2406    
Age                       -0.008560   0.010390  -0.824   0.4110    
Gendermale                -0.137008   0.170390  -0.804   0.4223    
Hypertension.compositeyes -0.077102   0.218758  -0.352   0.7249    
DiabetesStatusDiabetes    -0.347297   0.210691  -1.648   0.1008    
SmokerCurrentyes          -0.008344   0.158557  -0.053   0.9581    
Med.Statin.LLDyes         -0.081304   0.160921  -0.505   0.6139    
Med.all.antiplateletyes   -0.161602   0.287511  -0.562   0.5747    
GFR_MDRD                   0.007092   0.004574   1.551   0.1226    
BMI                       -0.007852   0.019866  -0.395   0.6931    
CAD_history                0.307826   0.162579   1.893   0.0597 .  
Stroke_history            -0.083226   0.160434  -0.519   0.6045    
Peripheral.interv          0.072404   0.180065   0.402   0.6880    
stenose50-70%             -2.941763   1.216350  -2.419   0.0165 *  
stenose70-90%             -2.647244   1.081787  -2.447   0.0153 *  
stenose90-99%             -2.691171   1.078445  -2.495   0.0134 *  
stenose100% (Occlusion)   -3.243744   1.269621  -2.555   0.0114 *  
hsCRP_plasma               0.002608   0.001766   1.477   0.1413    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.046 on 201 degrees of freedom
Multiple R-squared:  0.09531,   Adjusted R-squared:  0.0143 
F-statistic: 1.176 on 18 and 201 DF,  p-value: 0.2834

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: -0.042782 
Standard error............: 0.036352 
Odds ratio (effect size)..: 0.958 
Lower 95% CI..............: 0.892 
Upper 95% CI..............: 1.029 
T-value...................: -1.176897 
P-value...................: 0.2406286 
R^2.......................: 0.095313 
Adjusted r^2..............: 0.014297 
Sample size of AE DB......: 2388 
Sample size of model......: 220 
Missing data %............: 90.78727 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD + 
    Peripheral.interv, data = currentDF)

Coefficients:
           (Intercept)  DiabetesStatusDiabetes                GFR_MDRD       Peripheral.interv  
              3.513131               -0.349405                0.006456                0.283408  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0429 -0.7102 -0.0068  0.7129  3.6927 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                7.5280157  1.5680212   4.801 3.02e-06 ***
currentDF[, TRAIT]        -0.0582733  0.0534464  -1.090   0.2768    
Age                       -0.0115157  0.0099316  -1.159   0.2476    
Gendermale                -0.1584798  0.1660816  -0.954   0.3411    
Hypertension.compositeyes -0.1237622  0.2072961  -0.597   0.5511    
DiabetesStatusDiabetes    -0.3232686  0.2061713  -1.568   0.1184    
SmokerCurrentyes          -0.0725585  0.1555184  -0.467   0.6413    
Med.Statin.LLDyes         -0.1180983  0.1583704  -0.746   0.4567    
Med.all.antiplateletyes   -0.1487709  0.2863161  -0.520   0.6039    
GFR_MDRD                   0.0063622  0.0045939   1.385   0.1676    
BMI                       -0.0154651  0.0190781  -0.811   0.4185    
CAD_history                0.2714892  0.1624776   1.671   0.0962 .  
Stroke_history            -0.0335464  0.1577675  -0.213   0.8318    
Peripheral.interv          0.1904502  0.1814237   1.050   0.2951    
stenose50-70%             -2.6864090  1.2205761  -2.201   0.0288 *  
stenose70-90%             -2.3757456  1.0827885  -2.194   0.0293 *  
stenose90-99%             -2.4339008  1.0785472  -2.257   0.0251 *  
stenose100% (Occlusion)   -3.0893634  1.2658256  -2.441   0.0155 *  
hsCRP_plasma               0.0003386  0.0007429   0.456   0.6491    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.047 on 207 degrees of freedom
Multiple R-squared:  0.08992,   Adjusted R-squared:  0.01078 
F-statistic: 1.136 on 18 and 207 DF,  p-value: 0.3191

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.058273 
Standard error............: 0.053446 
Odds ratio (effect size)..: 0.943 
Lower 95% CI..............: 0.85 
Upper 95% CI..............: 1.048 
T-value...................: -1.090312 
P-value...................: 0.2768431 
R^2.......................: 0.08992 
Adjusted r^2..............: 0.010783 
Sample size of AE DB......: 2388 
Sample size of model......: 226 
Missing data %............: 90.53601 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ DiabetesStatus + GFR_MDRD + 
    CAD_history, data = currentDF)

Coefficients:
           (Intercept)  DiabetesStatusDiabetes                GFR_MDRD             CAD_history  
               3.31816                -0.33383                 0.00915                 0.22071  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.8724 -0.7374 -0.0104  0.7630  3.5688 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                7.8007864  1.5987118   4.879 2.16e-06 ***
currentDF[, TRAIT]        -0.1517641  0.1088994  -1.394   0.1650    
Age                       -0.0130366  0.0099970  -1.304   0.1937    
Gendermale                -0.1134130  0.1662476  -0.682   0.4959    
Hypertension.compositeyes -0.0308333  0.2126787  -0.145   0.8849    
DiabetesStatusDiabetes    -0.3325556  0.2078262  -1.600   0.1111    
SmokerCurrentyes          -0.0433421  0.1550346  -0.280   0.7801    
Med.Statin.LLDyes         -0.0542840  0.1579050  -0.344   0.7314    
Med.all.antiplateletyes   -0.0819246  0.2909088  -0.282   0.7785    
GFR_MDRD                   0.0070069  0.0045972   1.524   0.1290    
BMI                       -0.0124769  0.0189975  -0.657   0.5121    
CAD_history                0.2953265  0.1628940   1.813   0.0713 .  
Stroke_history            -0.0637634  0.1565213  -0.407   0.6842    
Peripheral.interv          0.1249545  0.1795735   0.696   0.4873    
stenose50-70%             -2.9441863  1.2085588  -2.436   0.0157 *  
stenose70-90%             -2.6033879  1.0757426  -2.420   0.0164 *  
stenose90-99%             -2.6655527  1.0725854  -2.485   0.0138 *  
stenose100% (Occlusion)   -2.4618653  1.3467240  -1.828   0.0690 .  
hsCRP_plasma               0.0004629  0.0007375   0.628   0.5310    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.038 on 201 degrees of freedom
Multiple R-squared:  0.1014,    Adjusted R-squared:  0.02092 
F-statistic:  1.26 on 18 and 201 DF,  p-value: 0.2177

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.151764 
Standard error............: 0.108899 
Odds ratio (effect size)..: 0.859 
Lower 95% CI..............: 0.694 
Upper 95% CI..............: 1.064 
T-value...................: -1.393618 
P-value...................: 0.164972 
R^2.......................: 0.101392 
Adjusted r^2..............: 0.02092 
Sample size of AE DB......: 2388 
Sample size of model......: 220 
Missing data %............: 90.78727 

Analysis of MCP1_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + Hypertension.composite + 
    Med.Statin.LLD, data = currentDF)

Coefficients:
              (Intercept)                        Age                 Gendermale  Hypertension.compositeyes          Med.Statin.LLDyes  
                  5.55361                   -0.01045                    0.34756                   -0.28582                   -0.23751  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2514 -0.5954  0.1000  0.5746  1.8421 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                7.230223   1.238018   5.840  1.6e-08 ***
currentDF[, TRAIT]         0.023207   0.027834   0.834  0.40521    
Age                       -0.014468   0.007419  -1.950  0.05229 .  
Gendermale                 0.332433   0.123271   2.697  0.00747 ** 
Hypertension.compositeyes -0.329740   0.157218  -2.097  0.03696 *  
DiabetesStatusDiabetes    -0.100619   0.150749  -0.667  0.50509    
SmokerCurrentyes          -0.097936   0.117929  -0.830  0.40706    
Med.Statin.LLDyes         -0.245651   0.122705  -2.002  0.04636 *  
Med.all.antiplateletyes   -0.049214   0.214926  -0.229  0.81907    
GFR_MDRD                  -0.000930   0.003266  -0.285  0.77608    
BMI                       -0.008874   0.014138  -0.628  0.53077    
CAD_history                0.130210   0.122869   1.060  0.29027    
Stroke_history            -0.069389   0.119888  -0.579  0.56325    
Peripheral.interv         -0.127172   0.136891  -0.929  0.35378    
stenose50-70%             -1.096950   0.983505  -1.115  0.26576    
stenose70-90%             -0.885463   0.896243  -0.988  0.32411    
stenose90-99%             -0.915352   0.895230  -1.022  0.30754    
stenose100% (Occlusion)   -2.251892   1.040082  -2.165  0.03132 *  
hsCRP_plasma              -0.000126   0.001446  -0.087  0.93063    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8735 on 252 degrees of freedom
Multiple R-squared:  0.1049,    Adjusted R-squared:  0.04097 
F-statistic: 1.641 on 18 and 252 DF,  p-value: 0.05087

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0.023206 
Standard error............: 0.027834 
Odds ratio (effect size)..: 1.023 
Lower 95% CI..............: 0.969 
Upper 95% CI..............: 1.081 
T-value...................: 0.833751 
P-value...................: 0.4052109 
R^2.......................: 0.104906 
Adjusted r^2..............: 0.040971 
Sample size of AE DB......: 2388 
Sample size of model......: 271 
Missing data %............: 88.65159 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + Med.Statin.LLD + stenose, 
    data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale  Hypertension.compositeyes  
                  6.62249                   -0.10860                   -0.01512                    0.37129                   -0.30505  
        Med.Statin.LLDyes              stenose50-70%              stenose70-90%              stenose90-99%    stenose100% (Occlusion)  
                 -0.24448                   -0.76334                   -0.72115                   -0.71934                   -2.12656  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.10855 -0.58245  0.01592  0.59716  1.91247 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                7.294e+00  1.196e+00   6.099 3.89e-09 ***
currentDF[, TRAIT]        -1.086e-01  3.656e-02  -2.971  0.00325 ** 
Age                       -1.758e-02  7.046e-03  -2.496  0.01320 *  
Gendermale                 3.277e-01  1.187e-01   2.760  0.00620 ** 
Hypertension.compositeyes -3.428e-01  1.483e-01  -2.312  0.02155 *  
DiabetesStatusDiabetes    -8.723e-02  1.464e-01  -0.596  0.55176    
SmokerCurrentyes          -1.007e-01  1.130e-01  -0.891  0.37373    
Med.Statin.LLDyes         -2.400e-01  1.189e-01  -2.018  0.04460 *  
Med.all.antiplateletyes   -8.667e-02  2.097e-01  -0.413  0.67969    
GFR_MDRD                   2.996e-05  3.182e-03   0.009  0.99250    
BMI                       -5.124e-03  1.346e-02  -0.381  0.70382    
CAD_history                1.832e-01  1.202e-01   1.524  0.12871    
Stroke_history            -1.968e-02  1.158e-01  -0.170  0.86514    
Peripheral.interv         -1.622e-01  1.339e-01  -1.211  0.22711    
stenose50-70%             -1.037e+00  9.639e-01  -1.076  0.28306    
stenose70-90%             -9.141e-01  8.774e-01  -1.042  0.29843    
stenose90-99%             -9.206e-01  8.757e-01  -1.051  0.29414    
stenose100% (Occlusion)   -2.393e+00  1.016e+00  -2.356  0.01925 *  
hsCRP_plasma               1.276e-04  6.039e-04   0.211  0.83279    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8557 on 258 degrees of freedom
Multiple R-squared:  0.1382,    Adjusted R-squared:  0.07811 
F-statistic: 2.299 on 18 and 258 DF,  p-value: 0.002402

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.108601 
Standard error............: 0.036558 
Odds ratio (effect size)..: 0.897 
Lower 95% CI..............: 0.835 
Upper 95% CI..............: 0.964 
T-value...................: -2.970624 
P-value...................: 0.003252192 
R^2.......................: 0.138234 
Adjusted r^2..............: 0.07811 
Sample size of AE DB......: 2388 
Sample size of model......: 277 
Missing data %............: 88.40033 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + Hypertension.composite + 
    Med.Statin.LLD + stenose, data = currentDF)

Coefficients:
              (Intercept)                        Age                 Gendermale  Hypertension.compositeyes          Med.Statin.LLDyes  
                  6.45526                   -0.01313                    0.42480                   -0.29184                   -0.24996  
            stenose50-70%              stenose70-90%              stenose90-99%    stenose100% (Occlusion)  
                 -0.88050                   -0.74244                   -0.74446                   -2.08812  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2211 -0.5497  0.0680  0.5714  1.9122 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                7.2769935  1.2463069   5.839 1.61e-08 ***
currentDF[, TRAIT]        -0.0485649  0.0770553  -0.630  0.52909    
Age                       -0.0153724  0.0072135  -2.131  0.03405 *  
Gendermale                 0.3816541  0.1223969   3.118  0.00203 ** 
Hypertension.compositeyes -0.3179798  0.1523841  -2.087  0.03792 *  
DiabetesStatusDiabetes    -0.0979577  0.1491193  -0.657  0.51184    
SmokerCurrentyes          -0.1164997  0.1155608  -1.008  0.31436    
Med.Statin.LLDyes         -0.2397153  0.1214925  -1.973  0.04957 *  
Med.all.antiplateletyes   -0.0723110  0.2129965  -0.339  0.73452    
GFR_MDRD                   0.0002343  0.0032848   0.071  0.94320    
BMI                       -0.0081189  0.0136832  -0.593  0.55348    
CAD_history                0.1596758  0.1233051   1.295  0.19651    
Stroke_history            -0.0527180  0.1178355  -0.447  0.65498    
Peripheral.interv         -0.1166770  0.1375548  -0.848  0.39712    
stenose50-70%             -1.1744730  0.9795354  -1.199  0.23165    
stenose70-90%             -0.9491066  0.8939936  -1.062  0.28941    
stenose90-99%             -0.9603463  0.8920512  -1.077  0.28270    
stenose100% (Occlusion)   -2.3642172  1.0347919  -2.285  0.02316 *  
hsCRP_plasma               0.0001594  0.0006141   0.259  0.79547    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8689 on 253 degrees of freedom
Multiple R-squared:  0.1154,    Adjusted R-squared:  0.05245 
F-statistic: 1.833 on 18 and 253 DF,  p-value: 0.02208

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.048565 
Standard error............: 0.077055 
Odds ratio (effect size)..: 0.953 
Lower 95% CI..............: 0.819 
Upper 95% CI..............: 1.108 
T-value...................: -0.63026 
P-value...................: 0.5290932 
R^2.......................: 0.115389 
Adjusted r^2..............: 0.052452 
Sample size of AE DB......: 2388 
Sample size of model......: 272 
Missing data %............: 88.60971 

Analysis of IL6_pg_ug_2015_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + CAD_history + 
    Stroke_history, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]         CAD_history      Stroke_history  
           -3.0888              0.1093             -0.3774              0.3903  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.5508 -0.8878  0.0457  0.8449  4.8428 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -2.4222537  1.2000578  -2.018 0.044005 *  
currentDF[, TRAIT]         0.1088754  0.0316577   3.439 0.000626 ***
Age                       -0.0089860  0.0074745  -1.202 0.229772    
Gendermale                 0.0975047  0.1296461   0.752 0.452306    
Hypertension.compositeyes -0.0224749  0.1802234  -0.125 0.900800    
DiabetesStatusDiabetes     0.0386768  0.1479191   0.261 0.793821    
SmokerCurrentyes           0.0852958  0.1298546   0.657 0.511535    
Med.Statin.LLDyes         -0.1932175  0.1432745  -1.349 0.177998    
Med.all.antiplateletyes    0.0843741  0.2046690   0.412 0.680311    
GFR_MDRD                  -0.0032847  0.0032672  -1.005 0.315152    
BMI                       -0.0184962  0.0160116  -1.155 0.248496    
CAD_history               -0.3320098  0.1363873  -2.434 0.015221 *  
Stroke_history             0.3735926  0.1282652   2.913 0.003722 ** 
Peripheral.interv         -0.0317326  0.1514970  -0.209 0.834163    
stenose50-70%              0.4969845  0.8633049   0.576 0.565057    
stenose70-90%              0.7618668  0.8303871   0.917 0.359271    
stenose90-99%              0.5369724  0.8302994   0.647 0.518069    
stenose100% (Occlusion)    1.0861297  1.0996247   0.988 0.323699    
stenose70-99%              1.4968708  1.1666027   1.283 0.199970    
hsCRP_plasma               0.0003995  0.0005636   0.709 0.478709    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.423 on 579 degrees of freedom
Multiple R-squared:  0.07298,   Adjusted R-squared:  0.04256 
F-statistic: 2.399 on 19 and 579 DF,  p-value: 0.0008015

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0.108875 
Standard error............: 0.031658 
Odds ratio (effect size)..: 1.115 
Lower 95% CI..............: 1.048 
Upper 95% CI..............: 1.186 
T-value...................: 3.439142 
P-value...................: 0.0006255322 
R^2.......................: 0.072977 
Adjusted r^2..............: 0.042557 
Sample size of AE DB......: 2388 
Sample size of model......: 599 
Missing data %............: 74.91625 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + CAD_history + Stroke_history, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes         CAD_history      Stroke_history  
          -2.27598            -0.13586            -0.01229            -0.21418            -0.28862             0.41719  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.3182 -0.8724  0.0207  0.8745  4.5800 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -2.5608054  1.1899666  -2.152  0.03181 * 
currentDF[, TRAIT]        -0.1279810  0.0417212  -3.068  0.00226 **
Age                       -0.0136454  0.0073632  -1.853  0.06436 . 
Gendermale                 0.0953330  0.1289844   0.739  0.46014   
Hypertension.compositeyes  0.0065689  0.1771091   0.037  0.97043   
DiabetesStatusDiabetes     0.0604255  0.1478941   0.409  0.68300   
SmokerCurrentyes           0.0614024  0.1288767   0.476  0.63394   
Med.Statin.LLDyes         -0.2091099  0.1414530  -1.478  0.13986   
Med.all.antiplateletyes    0.0606548  0.2050098   0.296  0.76744   
GFR_MDRD                  -0.0023243  0.0032865  -0.707  0.47970   
BMI                       -0.0118293  0.0157425  -0.751  0.45270   
CAD_history               -0.3029891  0.1361979  -2.225  0.02649 * 
Stroke_history             0.3925245  0.1273728   3.082  0.00215 **
Peripheral.interv         -0.0479725  0.1512385  -0.317  0.75121   
stenose50-70%              0.4905981  0.8624027   0.569  0.56966   
stenose70-90%              0.7947610  0.8310592   0.956  0.33930   
stenose90-99%              0.6256534  0.8314988   0.752  0.45209   
stenose100% (Occlusion)    1.0784576  1.0999095   0.980  0.32725   
stenose70-99%              1.6124436  1.1671141   1.382  0.16763   
hsCRP_plasma               0.0004751  0.0005016   0.947  0.34395   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.423 on 586 degrees of freedom
Multiple R-squared:  0.0681,    Adjusted R-squared:  0.03788 
F-statistic: 2.254 on 19 and 586 DF,  p-value: 0.001815

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.127981 
Standard error............: 0.041721 
Odds ratio (effect size)..: 0.88 
Lower 95% CI..............: 0.811 
Upper 95% CI..............: 0.955 
T-value...................: -3.067533 
P-value...................: 0.002257945 
R^2.......................: 0.068098 
Adjusted r^2..............: 0.037882 
Sample size of AE DB......: 2388 
Sample size of model......: 606 
Missing data %............: 74.62312 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD + 
    CAD_history + Stroke_history, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]   Med.Statin.LLDyes         CAD_history      Stroke_history  
           -2.8844             -0.1347             -0.2053             -0.3254              0.4094  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.4486 -0.8206  0.0114  0.8406  4.5942 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -2.1804508  1.2366293  -1.763  0.07844 . 
currentDF[, TRAIT]        -0.1363934  0.0698575  -1.952  0.05141 . 
Age                       -0.0090416  0.0077252  -1.170  0.24236   
Gendermale                 0.0851619  0.1349328   0.631  0.52822   
Hypertension.compositeyes -0.0401191  0.1873976  -0.214  0.83056   
DiabetesStatusDiabetes     0.0529991  0.1572577   0.337  0.73623   
SmokerCurrentyes           0.1252375  0.1365104   0.917  0.35934   
Med.Statin.LLDyes         -0.2091830  0.1489028  -1.405  0.16066   
Med.all.antiplateletyes   -0.0138050  0.2249842  -0.061  0.95110   
GFR_MDRD                  -0.0025894  0.0035298  -0.734  0.46353   
BMI                       -0.0152657  0.0163816  -0.932  0.35182   
CAD_history               -0.2779734  0.1444831  -1.924  0.05490 . 
Stroke_history             0.3829146  0.1349112   2.838  0.00471 **
Peripheral.interv         -0.0375603  0.1617412  -0.232  0.81645   
stenose50-70%              0.1874127  0.8794295   0.213  0.83133   
stenose70-90%              0.5433789  0.8400991   0.647  0.51804   
stenose90-99%              0.3455578  0.8393161   0.412  0.68072   
stenose100% (Occlusion)    0.8137303  1.1099752   0.733  0.46382   
stenose70-99%              2.9446594  1.6628302   1.771  0.07715 . 
hsCRP_plasma               0.0006493  0.0008577   0.757  0.44933   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.433 on 532 degrees of freedom
Multiple R-squared:  0.06303,   Adjusted R-squared:  0.02956 
F-statistic: 1.883 on 19 and 532 DF,  p-value: 0.01328

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.136393 
Standard error............: 0.069857 
Odds ratio (effect size)..: 0.872 
Lower 95% CI..............: 0.761 
Upper 95% CI..............: 1.001 
T-value...................: -1.952452 
P-value...................: 0.05140865 
R^2.......................: 0.063026 
Adjusted r^2..............: 0.029562 
Sample size of AE DB......: 2388 
Sample size of model......: 552 
Missing data %............: 76.88442 

Analysis of IL6R_pg_ug_2015_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + DiabetesStatus + 
    Med.Statin.LLD + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]   DiabetesStatusDiabetes        Med.Statin.LLDyes              CAD_history  
               -2.72515                  0.12169                 -0.23102                 -0.41274                 -0.17988  
         Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  
                0.21758                  0.32738                  1.02141                  1.04648                  1.31783  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
                0.80210                  1.02716                 -0.06394  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.2446 -0.5444  0.1137  0.7057  2.9730 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -1.5498124  1.0648708  -1.455 0.146097    
currentDF[, TRAIT]         0.1242813  0.0252771   4.917 1.15e-06 ***
Age                       -0.0062752  0.0060075  -1.045 0.296654    
Gendermale                -0.0379152  0.1036725  -0.366 0.714706    
Hypertension.compositeyes -0.0443839  0.1438683  -0.309 0.757809    
DiabetesStatusDiabetes    -0.2268754  0.1183648  -1.917 0.055758 .  
SmokerCurrentyes           0.0655855  0.1041078   0.630 0.528957    
Med.Statin.LLDyes         -0.4202807  0.1157337  -3.631 0.000307 ***
Med.all.antiplateletyes   -0.0478602  0.1645514  -0.291 0.771267    
GFR_MDRD                  -0.0035106  0.0026211  -1.339 0.180975    
BMI                       -0.0173525  0.0132633  -1.308 0.191286    
CAD_history               -0.1464790  0.1097927  -1.334 0.182678    
Stroke_history             0.2072312  0.1027701   2.016 0.044211 *  
Peripheral.interv          0.3040732  0.1217287   2.498 0.012765 *  
stenose50-70%              1.1008527  0.8402367   1.310 0.190654    
stenose70-90%              1.1052473  0.8182193   1.351 0.177285    
stenose90-99%              1.3678656  0.8184965   1.671 0.095220 .  
stenose100% (Occlusion)    0.7729226  1.0016549   0.772 0.440637    
stenose50-99%              1.1115231  1.1502332   0.966 0.334272    
stenose70-99%             -0.0056577  1.0502892  -0.005 0.995704    
hsCRP_plasma              -0.0002985  0.0004536  -0.658 0.510846    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.145 on 583 degrees of freedom
Multiple R-squared:  0.1217,    Adjusted R-squared:  0.09155 
F-statistic: 4.038 on 20 and 583 DF,  p-value: 1.365e-08

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0.124281 
Standard error............: 0.025277 
Odds ratio (effect size)..: 1.132 
Lower 95% CI..............: 1.078 
Upper 95% CI..............: 1.19 
T-value...................: 4.916746 
P-value...................: 1.145657e-06 
R^2.......................: 0.121682 
Adjusted r^2..............: 0.091551 
Sample size of AE DB......: 2388 
Sample size of model......: 604 
Missing data %............: 74.70687 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + DiabetesStatus + 
    Med.Statin.LLD + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]   DiabetesStatusDiabetes        Med.Statin.LLDyes              CAD_history  
               -2.89712                  0.05661                 -0.22774                 -0.41591                 -0.17720  
         Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  
                0.24162                  0.28469                  1.00305                  1.02737                  1.33187  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
                0.71296                  0.91858                 -0.03186  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.3697 -0.5251  0.0999  0.6793  3.0657 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -1.6616722  1.0734979  -1.548 0.122181    
currentDF[, TRAIT]         0.0541870  0.0343533   1.577 0.115252    
Age                       -0.0096233  0.0060105  -1.601 0.109891    
Gendermale                 0.0371287  0.1048229   0.354 0.723313    
Hypertension.compositeyes -0.0252319  0.1435207  -0.176 0.860506    
DiabetesStatusDiabetes    -0.2311323  0.1201514  -1.924 0.054876 .  
SmokerCurrentyes           0.0471135  0.1049359   0.449 0.653615    
Med.Statin.LLDyes         -0.4305494  0.1160480  -3.710 0.000227 ***
Med.all.antiplateletyes   -0.0452318  0.1673390  -0.270 0.787023    
GFR_MDRD                  -0.0036355  0.0026762  -1.358 0.174832    
BMI                       -0.0115840  0.0132147  -0.877 0.381061    
CAD_history               -0.1490517  0.1113280  -1.339 0.181134    
Stroke_history             0.2455585  0.1036470   2.369 0.018148 *  
Peripheral.interv          0.2647824  0.1233105   2.147 0.032178 *  
stenose50-70%              1.0495555  0.8529759   1.230 0.219014    
stenose70-90%              1.0503071  0.8317698   1.263 0.207182    
stenose90-99%              1.3454201  0.8324336   1.616 0.106575    
stenose100% (Occlusion)    0.6316689  1.0182788   0.620 0.535280    
stenose50-99%              0.9915459  1.1681242   0.849 0.396317    
stenose70-99%              0.0392846  1.0669644   0.037 0.970642    
hsCRP_plasma              -0.0002766  0.0004100  -0.675 0.500087    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.163 on 590 degrees of freedom
Multiple R-squared:  0.09125,   Adjusted R-squared:  0.06045 
F-statistic: 2.962 on 20 and 590 DF,  p-value: 1.844e-05

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: 0.054187 
Standard error............: 0.034353 
Odds ratio (effect size)..: 1.056 
Lower 95% CI..............: 0.987 
Upper 95% CI..............: 1.129 
T-value...................: 1.577347 
P-value...................: 0.1152517 
R^2.......................: 0.091252 
Adjusted r^2..............: 0.060446 
Sample size of AE DB......: 2388 
Sample size of model......: 611 
Missing data %............: 74.41374 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + DiabetesStatus + Med.Statin.LLD + 
    CAD_history + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Coefficients:
            (Intercept)                      Age   DiabetesStatusDiabetes        Med.Statin.LLDyes              CAD_history  
              -2.286903                -0.009033                -0.247772                -0.491583                -0.194727  
         Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  
               0.244739                 0.278970                 1.108588                 1.158943                 1.433943  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
               0.739330                 0.991161                -0.294824  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-7.3050 -0.5167  0.0747  0.6748  3.0675 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -1.627e+00  1.105e+00  -1.472   0.1415    
currentDF[, TRAIT]         5.713e-02  5.681e-02   1.006   0.3150    
Age                       -1.151e-02  6.294e-03  -1.828   0.0681 .  
Gendermale                 8.824e-03  1.092e-01   0.081   0.9356    
Hypertension.compositeyes -1.964e-02  1.517e-01  -0.129   0.8970    
DiabetesStatusDiabetes    -2.573e-01  1.273e-01  -2.021   0.0438 *  
SmokerCurrentyes           3.200e-02  1.108e-01   0.289   0.7728    
Med.Statin.LLDyes         -4.882e-01  1.218e-01  -4.007 7.02e-05 ***
Med.all.antiplateletyes   -9.108e-02  1.828e-01  -0.498   0.6185    
GFR_MDRD                  -3.389e-03  2.866e-03  -1.182   0.2376    
BMI                       -1.281e-02  1.385e-02  -0.925   0.3552    
CAD_history               -2.023e-01  1.175e-01  -1.721   0.0858 .  
Stroke_history             2.240e-01  1.093e-01   2.050   0.0409 *  
Peripheral.interv          2.654e-01  1.311e-01   2.024   0.0435 *  
stenose50-70%              1.232e+00  8.614e-01   1.430   0.1533    
stenose70-90%              1.238e+00  8.343e-01   1.484   0.1385    
stenose90-99%              1.501e+00  8.343e-01   1.799   0.0726 .  
stenose100% (Occlusion)    7.336e-01  1.021e+00   0.719   0.4726    
stenose50-99%              1.069e+00  1.170e+00   0.914   0.3612    
stenose70-99%             -1.502e-01  1.437e+00  -0.105   0.9168    
hsCRP_plasma               5.195e-05  6.976e-04   0.074   0.9407    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.165 on 533 degrees of freedom
Multiple R-squared:  0.09438,   Adjusted R-squared:  0.0604 
F-statistic: 2.777 on 20 and 533 DF,  p-value: 6.359e-05

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: 0.057125 
Standard error............: 0.056805 
Odds ratio (effect size)..: 1.059 
Lower 95% CI..............: 0.947 
Upper 95% CI..............: 1.183 
T-value...................: 1.005637 
P-value...................: 0.3150463 
R^2.......................: 0.094382 
Adjusted r^2..............: 0.0604 
Sample size of AE DB......: 2388 
Sample size of model......: 554 
Missing data %............: 76.80067 

Analysis of MCP1_pg_ug_2015_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD + Stroke_history + 
    stenose, data = currentDF)

Coefficients:
            (Intercept)        Med.Statin.LLDyes           Stroke_history            stenose50-70%            stenose70-90%  
                -1.8793                  -0.2912                   0.3131                   1.1220                   1.0316  
          stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
                 0.7273                  -0.4113                   0.8206                   1.5138  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.3039 -0.7758  0.0643  0.8035  3.3860 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -0.8812033  1.0731857  -0.821   0.4119  
currentDF[, TRAIT]        -0.0295689  0.0277731  -1.065   0.2875  
Age                       -0.0042653  0.0066191  -0.644   0.5196  
Gendermale                 0.1462051  0.1139403   1.283   0.1999  
Hypertension.compositeyes -0.1960709  0.1588148  -1.235   0.2175  
DiabetesStatusDiabetes    -0.1429423  0.1298394  -1.101   0.2714  
SmokerCurrentyes          -0.1277565  0.1141235  -1.119   0.2634  
Med.Statin.LLDyes         -0.2610838  0.1278712  -2.042   0.0416 *
Med.all.antiplateletyes   -0.0781823  0.1808369  -0.432   0.6657  
GFR_MDRD                  -0.0029277  0.0028995  -1.010   0.3130  
BMI                       -0.0116576  0.0141153  -0.826   0.4092  
CAD_history               -0.1361524  0.1197991  -1.137   0.2562  
Stroke_history             0.2928078  0.1137000   2.575   0.0103 *
Peripheral.interv          0.0717310  0.1334881   0.537   0.5912  
stenose50-70%              1.0601418  0.7766663   1.365   0.1728  
stenose70-90%              1.0090563  0.7470111   1.351   0.1773  
stenose90-99%              0.7176677  0.7470206   0.961   0.3371  
stenose100% (Occlusion)   -0.5769898  0.9891836  -0.583   0.5599  
stenose50-99%              0.9900753  1.1755751   0.842   0.4000  
stenose70-99%              1.5784674  1.0497797   1.504   0.1332  
hsCRP_plasma               0.0007688  0.0005068   1.517   0.1298  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.28 on 602 degrees of freedom
Multiple R-squared:  0.06338,   Adjusted R-squared:  0.03226 
F-statistic: 2.037 on 20 and 602 DF,  p-value: 0.004992

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: -0.029569 
Standard error............: 0.027773 
Odds ratio (effect size)..: 0.971 
Lower 95% CI..............: 0.919 
Upper 95% CI..............: 1.025 
T-value...................: -1.064662 
P-value...................: 0.2874558 
R^2.......................: 0.063381 
Adjusted r^2..............: 0.032264 
Sample size of AE DB......: 2388 
Sample size of model......: 623 
Missing data %............: 73.91122 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + DiabetesStatus + 
    Med.Statin.LLD + Stroke_history + stenose, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]   DiabetesStatusDiabetes        Med.Statin.LLDyes           Stroke_history  
               -1.88268                 -0.09171                 -0.18016                 -0.29820                  0.28716  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%  
                1.12462                  1.07530                  0.80391                 -0.38417                  0.90149  
          stenose70-99%  
                1.56396  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1184 -0.7854  0.0493  0.8138  3.2620 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)               -0.6700995  1.0642423  -0.630   0.5292  
currentDF[, TRAIT]        -0.0872578  0.0368329  -2.369   0.0181 *
Age                       -0.0066517  0.0065169  -1.021   0.3078  
Gendermale                 0.0923885  0.1136494   0.813   0.4166  
Hypertension.compositeyes -0.1893488  0.1561421  -1.213   0.2257  
DiabetesStatusDiabetes    -0.1700329  0.1298157  -1.310   0.1908  
SmokerCurrentyes          -0.1389901  0.1132600  -1.227   0.2202  
Med.Statin.LLDyes         -0.2803065  0.1261178  -2.223   0.0266 *
Med.all.antiplateletyes   -0.1139180  0.1811412  -0.629   0.5297  
GFR_MDRD                  -0.0024052  0.0029160  -0.825   0.4098  
BMI                       -0.0130704  0.0138807  -0.942   0.3468  
CAD_history               -0.1011589  0.1195612  -0.846   0.3978  
Stroke_history             0.2824235  0.1129619   2.500   0.0127 *
Peripheral.interv          0.0863623  0.1332206   0.648   0.5171  
stenose50-70%              1.1040728  0.7757153   1.423   0.1552  
stenose70-90%              1.0748336  0.7474338   1.438   0.1509  
stenose90-99%              0.8133478  0.7479632   1.087   0.2773  
stenose100% (Occlusion)   -0.4990121  0.9892412  -0.504   0.6141  
stenose50-99%              1.0758127  1.1750666   0.916   0.3603  
stenose70-99%              1.6605722  1.0500528   1.581   0.1143  
hsCRP_plasma               0.0005881  0.0004510   1.304   0.1927  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.28 on 609 degrees of freedom
Multiple R-squared:  0.06973,   Adjusted R-squared:  0.03918 
F-statistic: 2.282 on 20 and 609 DF,  p-value: 0.001226

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.087258 
Standard error............: 0.036833 
Odds ratio (effect size)..: 0.916 
Lower 95% CI..............: 0.853 
Upper 95% CI..............: 0.985 
T-value...................: -2.369014 
P-value...................: 0.01814655 
R^2.......................: 0.069727 
Adjusted r^2..............: 0.039176 
Sample size of AE DB......: 2388 
Sample size of model......: 630 
Missing data %............: 73.61809 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD + 
    Stroke_history, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]   Med.Statin.LLDyes      Stroke_history  
           -0.7804             -0.1472             -0.2899              0.3511  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.1603 -0.7207  0.0469  0.8139  3.3145 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)               -3.642e-01  1.120e+00  -0.325  0.74524   
currentDF[, TRAIT]        -1.187e-01  6.272e-02  -1.893  0.05885 . 
Age                       -6.080e-03  6.935e-03  -0.877  0.38104   
Gendermale                 1.729e-01  1.203e-01   1.437  0.15123   
Hypertension.compositeyes -1.658e-01  1.688e-01  -0.982  0.32658   
DiabetesStatusDiabetes    -1.479e-01  1.399e-01  -1.057  0.29094   
SmokerCurrentyes          -1.308e-01  1.216e-01  -1.076  0.28225   
Med.Statin.LLDyes         -2.624e-01  1.350e-01  -1.943  0.05255 . 
Med.all.antiplateletyes   -6.918e-02  2.005e-01  -0.345  0.73022   
GFR_MDRD                  -3.295e-03  3.167e-03  -1.040  0.29872   
BMI                       -1.416e-02  1.475e-02  -0.960  0.33764   
CAD_history               -1.212e-01  1.289e-01  -0.940  0.34746   
Stroke_history             3.304e-01  1.211e-01   2.728  0.00657 **
Peripheral.interv          2.054e-02  1.440e-01   0.143  0.88663   
stenose50-70%              8.481e-01  8.003e-01   1.060  0.28977   
stenose70-90%              8.732e-01  7.645e-01   1.142  0.25389   
stenose90-99%              6.220e-01  7.639e-01   0.814  0.41586   
stenose100% (Occlusion)   -6.670e-01  1.010e+00  -0.660  0.50926   
stenose50-99%              1.029e+00  1.198e+00   0.859  0.39075   
stenose70-99%              1.565e+00  1.513e+00   1.034  0.30148   
hsCRP_plasma              -1.871e-05  7.805e-04  -0.024  0.98088   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.305 on 551 degrees of freedom
Multiple R-squared:  0.06408,   Adjusted R-squared:  0.0301 
F-statistic: 1.886 on 20 and 551 DF,  p-value: 0.01145

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.118742 
Standard error............: 0.06272 
Odds ratio (effect size)..: 0.888 
Lower 95% CI..............: 0.785 
Upper 95% CI..............: 1.004 
T-value...................: -1.893206 
P-value...................: 0.05885423 
R^2.......................: 0.064076 
Adjusted r^2..............: 0.030104 
Sample size of AE DB......: 2388 
Sample size of model......: 572 
Missing data %............: 76.0469 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    Med.all.antiplatelet + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes    Med.all.antiplateletyes          Peripheral.interv  
                  -1.0880                     0.7922                     0.8796                    -0.4787  

Degrees of Freedom: 227 Total (i.e. Null);  224 Residual
Null Deviance:      311 
Residual Deviance: 302  AIC: 310

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9104  -1.1827   0.7429   1.0011   1.9740  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -1.467e+01  8.827e+02  -0.017   0.9867  
currentDF[, PROTEIN]       8.888e-02  1.398e-01   0.636   0.5248  
Age                       -6.796e-03  2.019e-02  -0.337   0.7365  
Gendermale                -1.165e-01  3.334e-01  -0.349   0.7268  
Hypertension.compositeyes  1.044e+00  4.298e-01   2.429   0.0151 *
DiabetesStatusDiabetes    -4.304e-01  4.153e-01  -1.036   0.3001  
SmokerCurrentyes          -2.732e-01  3.105e-01  -0.880   0.3789  
Med.Statin.LLDyes         -1.268e-01  3.186e-01  -0.398   0.6907  
Med.all.antiplateletyes    1.237e+00  6.093e-01   2.031   0.0423 *
GFR_MDRD                  -8.006e-03  9.252e-03  -0.865   0.3869  
BMI                       -3.583e-02  3.846e-02  -0.931   0.3516  
CAD_history                4.087e-01  3.338e-01   1.224   0.2209  
Stroke_history             3.007e-02  3.184e-01   0.094   0.9247  
Peripheral.interv         -4.737e-01  3.534e-01  -1.340   0.1801  
stenose50-70%              1.621e+01  8.827e+02   0.018   0.9854  
stenose70-90%              1.527e+01  8.827e+02   0.017   0.9862  
stenose90-99%              1.459e+01  8.827e+02   0.017   0.9868  
stenose100% (Occlusion)    1.619e+01  8.827e+02   0.018   0.9854  
hsCRP_plasma               2.381e-04  1.477e-03   0.161   0.8719  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 310.99  on 227  degrees of freedom
Residual deviance: 289.33  on 209  degrees of freedom
AIC: 327.33

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.088879 
Standard error............: 0.139763 
Odds ratio (effect size)..: 1.093 
Lower 95% CI..............: 0.831 
Upper 95% CI..............: 1.437 
Z-value...................: 0.635922 
P-value...................: 0.5248275 
Hosmer and Lemeshow r^2...: 0.069621 
Cox and Snell r^2.........: 0.090591 
Nagelkerke's pseudo r^2...: 0.121704 
Sample size of AE DB......: 2388 
Sample size of model......: 228 
Missing data %............: 90.45226 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)  
      1.164  

Degrees of Freedom: 226 Total (i.e. Null);  226 Residual
Null Deviance:      249.1 
Residual Deviance: 249.1    AIC: 251.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-1.96913   0.00035   0.65960   0.76941   1.25599  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.807e+01  2.400e+03   0.008   0.9940  
currentDF[, PROTEIN]      -4.345e-02  1.592e-01  -0.273   0.7849  
Age                       -3.032e-04  2.255e-02  -0.013   0.9893  
Gendermale                -1.847e-01  3.851e-01  -0.480   0.6314  
Hypertension.compositeyes  1.506e-01  4.760e-01   0.316   0.7517  
DiabetesStatusDiabetes     2.247e-01  4.826e-01   0.466   0.6414  
SmokerCurrentyes           2.939e-01  3.591e-01   0.819   0.4130  
Med.Statin.LLDyes         -2.610e-01  3.679e-01  -0.710   0.4780  
Med.all.antiplateletyes    1.054e+00  5.928e-01   1.778   0.0755 .
GFR_MDRD                  -7.672e-03  1.060e-02  -0.724   0.4693  
BMI                       -5.653e-02  4.244e-02  -1.332   0.1829  
CAD_history                2.094e-01  3.775e-01   0.555   0.5792  
Stroke_history             3.602e-01  3.784e-01   0.952   0.3411  
Peripheral.interv         -2.213e-01  3.930e-01  -0.563   0.5733  
stenose50-70%             -3.942e-01  2.671e+03   0.000   0.9999  
stenose70-90%             -1.559e+01  2.400e+03  -0.006   0.9948  
stenose90-99%             -1.589e+01  2.400e+03  -0.007   0.9947  
stenose100% (Occlusion)    2.999e-01  2.762e+03   0.000   0.9999  
hsCRP_plasma              -3.611e-05  1.754e-03  -0.021   0.9836  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 249.08  on 226  degrees of freedom
Residual deviance: 236.35  on 208  degrees of freedom
AIC: 274.35

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.043447 
Standard error............: 0.159165 
Odds ratio (effect size)..: 0.957 
Lower 95% CI..............: 0.701 
Upper 95% CI..............: 1.308 
Z-value...................: -0.272968 
P-value...................: 0.7848776 
Hosmer and Lemeshow r^2...: 0.051106 
Cox and Snell r^2.........: 0.054533 
Nagelkerke's pseudo r^2...: 0.081855 
Sample size of AE DB......: 2388 
Sample size of model......: 227 
Missing data %............: 90.49414 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)                 Gendermale  Hypertension.compositeyes  
                   0.0896                     0.5880                     1.1666  

Degrees of Freedom: 227 Total (i.e. Null);  225 Residual
Null Deviance:      223.7 
Residual Deviance: 213.1    AIC: 219.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3036   0.3624   0.5214   0.6599   1.2796  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.430e+01  2.400e+03   0.006  0.99525   
currentDF[, PROTEIN]       2.696e-02  1.700e-01   0.159  0.87400   
Age                       -4.561e-04  2.558e-02  -0.018  0.98577   
Gendermale                 6.874e-01  4.010e-01   1.714  0.08646 . 
Hypertension.compositeyes  1.198e+00  4.578e-01   2.617  0.00886 **
DiabetesStatusDiabetes    -4.552e-01  5.144e-01  -0.885  0.37630   
SmokerCurrentyes           4.572e-01  4.162e-01   1.098  0.27204   
Med.Statin.LLDyes          1.214e-01  4.076e-01   0.298  0.76579   
Med.all.antiplateletyes    3.352e-01  7.061e-01   0.475  0.63496   
GFR_MDRD                  -1.454e-02  1.220e-02  -1.191  0.23351   
BMI                        5.048e-02  5.070e-02   0.996  0.31943   
CAD_history               -4.837e-02  4.248e-01  -0.114  0.90935   
Stroke_history            -2.502e-01  4.069e-01  -0.615  0.53863   
Peripheral.interv         -4.368e-01  4.284e-01  -1.019  0.30798   
stenose50-70%             -1.654e+01  2.400e+03  -0.007  0.99450   
stenose70-90%             -1.461e+01  2.400e+03  -0.006  0.99514   
stenose90-99%             -1.511e+01  2.400e+03  -0.006  0.99498   
stenose100% (Occlusion)    1.041e+00  2.706e+03   0.000  0.99969   
hsCRP_plasma              -7.061e-04  1.826e-03  -0.387  0.69894   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 223.68  on 227  degrees of freedom
Residual deviance: 201.20  on 209  degrees of freedom
AIC: 239.2

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.026956 
Standard error............: 0.169988 
Odds ratio (effect size)..: 1.027 
Lower 95% CI..............: 0.736 
Upper 95% CI..............: 1.434 
Z-value...................: 0.158577 
P-value...................: 0.8740024 
Hosmer and Lemeshow r^2...: 0.100502 
Cox and Snell r^2.........: 0.093892 
Nagelkerke's pseudo r^2...: 0.150208 
Sample size of AE DB......: 2388 
Sample size of model......: 228 
Missing data %............: 90.45226 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)   Gendermale  
     0.6466       0.7704  

Degrees of Freedom: 227 Total (i.e. Null);  226 Residual
Null Deviance:      249.6 
Residual Deviance: 244.3    AIC: 248.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1948   0.2843   0.6345   0.7479   1.1973  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                12.719295 882.748263   0.014   0.9885  
currentDF[, PROTEIN]       -0.158259   0.154518  -1.024   0.3057  
Age                         0.021323   0.022751   0.937   0.3487  
Gendermale                  0.787010   0.365125   2.155   0.0311 *
Hypertension.compositeyes   0.556927   0.446202   1.248   0.2120  
DiabetesStatusDiabetes     -0.311891   0.462582  -0.674   0.5002  
SmokerCurrentyes            0.552119   0.367039   1.504   0.1325  
Med.Statin.LLDyes           0.032643   0.366678   0.089   0.9291  
Med.all.antiplateletyes    -0.322585   0.731270  -0.441   0.6591  
GFR_MDRD                    0.002551   0.010865   0.235   0.8143  
BMI                         0.010367   0.042470   0.244   0.8072  
CAD_history                 0.002075   0.380029   0.005   0.9956  
Stroke_history             -0.168600   0.368940  -0.457   0.6477  
Peripheral.interv          -0.094236   0.403477  -0.234   0.8153  
stenose50-70%             -13.221860 882.744597  -0.015   0.9880  
stenose70-90%             -13.718958 882.743674  -0.016   0.9876  
stenose90-99%             -13.629294 882.743648  -0.015   0.9877  
stenose100% (Occlusion)   -14.220158 882.744840  -0.016   0.9871  
hsCRP_plasma                0.005093   0.005303   0.960   0.3369  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 249.62  on 227  degrees of freedom
Residual deviance: 235.94  on 209  degrees of freedom
AIC: 273.94

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: IPH 
Effect size...............: -0.158259 
Standard error............: 0.154518 
Odds ratio (effect size)..: 0.854 
Lower 95% CI..............: 0.631 
Upper 95% CI..............: 1.156 
Z-value...................: -1.024214 
P-value...................: 0.3057341 
Hosmer and Lemeshow r^2...: 0.054812 
Cox and Snell r^2.........: 0.058244 
Nagelkerke's pseudo r^2...: 0.087533 
Sample size of AE DB......: 2388 
Sample size of model......: 228 
Missing data %............: 90.45226 

Analysis of MCP1_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    DiabetesStatus + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes     DiabetesStatusDiabetes          Peripheral.interv  
                  0.09271                    0.58088                   -0.65466                   -0.62140  

Degrees of Freedom: 278 Total (i.e. Null);  275 Residual
Null Deviance:      378.8 
Residual Deviance: 368.2    AIC: 376.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8758  -1.2049   0.7323   0.9929   1.9463  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -1.470e+01  8.827e+02  -0.017   0.9867  
currentDF[, PROTEIN]      -2.190e-01  1.532e-01  -1.430   0.1528  
Age                        1.112e-02  1.734e-02   0.641   0.5214  
Gendermale                 1.861e-01  2.961e-01   0.628   0.5298  
Hypertension.compositeyes  5.904e-01  3.662e-01   1.612   0.1069  
DiabetesStatusDiabetes    -8.080e-01  3.581e-01  -2.257   0.0240 *
SmokerCurrentyes           1.574e-01  2.809e-01   0.560   0.5752  
Med.Statin.LLDyes         -1.235e-01  2.946e-01  -0.419   0.6752  
Med.all.antiplateletyes    9.068e-01  5.242e-01   1.730   0.0836 .
GFR_MDRD                  -2.241e-03  7.880e-03  -0.284   0.7761  
BMI                        6.325e-03  3.309e-02   0.191   0.8484  
CAD_history                1.825e-01  2.990e-01   0.610   0.5417  
Stroke_history            -4.221e-01  2.847e-01  -1.483   0.1382  
Peripheral.interv         -6.863e-01  3.240e-01  -2.118   0.0342 *
stenose50-70%              1.472e+01  8.827e+02   0.017   0.9867  
stenose70-90%              1.465e+01  8.827e+02   0.017   0.9868  
stenose90-99%              1.398e+01  8.827e+02   0.016   0.9874  
stenose100% (Occlusion)    1.509e+01  8.827e+02   0.017   0.9864  
hsCRP_plasma               9.254e-05  1.564e-03   0.059   0.9528  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 378.82  on 278  degrees of freedom
Residual deviance: 354.39  on 260  degrees of freedom
AIC: 392.39

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.218974 
Standard error............: 0.153168 
Odds ratio (effect size)..: 0.803 
Lower 95% CI..............: 0.595 
Upper 95% CI..............: 1.085 
Z-value...................: -1.429634 
P-value...................: 0.1528221 
Hosmer and Lemeshow r^2...: 0.064487 
Cox and Snell r^2.........: 0.083836 
Nagelkerke's pseudo r^2...: 0.112869 
Sample size of AE DB......: 2388 
Sample size of model......: 279 
Missing data %............: 88.31658 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN], 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]  
              4.1409               -0.5963  

Degrees of Freedom: 277 Total (i.e. Null);  276 Residual
Null Deviance:      292.6 
Residual Deviance: 280.9    AIC: 284.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.40189   0.04367   0.59854   0.75261   1.27383  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                2.019e+01  2.400e+03   0.008  0.99329   
currentDF[, PROTEIN]      -6.303e-01  2.010e-01  -3.136  0.00171 **
Age                       -2.621e-03  2.060e-02  -0.127  0.89876   
Gendermale                -4.887e-02  3.644e-01  -0.134  0.89331   
Hypertension.compositeyes  2.694e-02  4.346e-01   0.062  0.95057   
DiabetesStatusDiabetes     1.557e-01  4.407e-01   0.353  0.72396   
SmokerCurrentyes           3.493e-01  3.437e-01   1.016  0.30954   
Med.Statin.LLDyes         -8.183e-03  3.463e-01  -0.024  0.98115   
Med.all.antiplateletyes    7.947e-01  5.644e-01   1.408  0.15911   
GFR_MDRD                  -5.321e-03  9.615e-03  -0.553  0.57996   
BMI                       -1.097e-02  4.108e-02  -0.267  0.78947   
CAD_history                1.156e-01  3.530e-01   0.327  0.74333   
Stroke_history             3.336e-01  3.580e-01   0.932  0.35152   
Peripheral.interv         -4.883e-01  3.716e-01  -1.314  0.18884   
stenose50-70%             -6.988e-01  2.624e+03   0.000  0.99979   
stenose70-90%             -1.575e+01  2.400e+03  -0.007  0.99476   
stenose90-99%             -1.611e+01  2.400e+03  -0.007  0.99464   
stenose100% (Occlusion)   -7.586e-01  2.699e+03   0.000  0.99978   
hsCRP_plasma              -7.904e-05  1.698e-03  -0.047  0.96288   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 292.56  on 277  degrees of freedom
Residual deviance: 269.41  on 259  degrees of freedom
AIC: 307.41

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.630266 
Standard error............: 0.200995 
Odds ratio (effect size)..: 0.532 
Lower 95% CI..............: 0.359 
Upper 95% CI..............: 0.79 
Z-value...................: -3.135732 
P-value...................: 0.001714256 
Hosmer and Lemeshow r^2...: 0.079123 
Cox and Snell r^2.........: 0.079893 
Nagelkerke's pseudo r^2...: 0.122745 
Sample size of AE DB......: 2388 
Sample size of model......: 278 
Missing data %............: 88.35846 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Hypertension.composite, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]                 Gendermale  Hypertension.compositeyes  
                  -2.9243                     0.6453                     0.6773                     1.0243  

Degrees of Freedom: 278 Total (i.e. Null);  275 Residual
Null Deviance:      290.5 
Residual Deviance: 264.7    AIC: 272.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3824   0.2949   0.4989   0.6958   1.6759  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                7.540e+00  8.827e+02   0.009 0.993185    
currentDF[, PROTEIN]       7.111e-01  1.940e-01   3.666 0.000246 ***
Age                        3.819e-03  2.146e-02   0.178 0.858758    
Gendermale                 7.301e-01  3.458e-01   2.111 0.034740 *  
Hypertension.compositeyes  9.482e-01  4.234e-01   2.240 0.025122 *  
DiabetesStatusDiabetes    -2.394e-01  4.376e-01  -0.547 0.584309    
SmokerCurrentyes           4.139e-01  3.533e-01   1.172 0.241352    
Med.Statin.LLDyes          1.285e-02  3.729e-01   0.034 0.972520    
Med.all.antiplateletyes    5.448e-01  5.944e-01   0.917 0.359365    
GFR_MDRD                  -7.860e-03  1.005e-02  -0.782 0.434221    
BMI                        3.652e-02  4.134e-02   0.883 0.377006    
CAD_history               -1.925e-02  3.799e-01  -0.051 0.959579    
Stroke_history             1.594e-01  3.668e-01   0.434 0.663940    
Peripheral.interv          1.323e-02  4.048e-01   0.033 0.973932    
stenose50-70%             -1.418e+01  8.827e+02  -0.016 0.987184    
stenose70-90%             -1.167e+01  8.827e+02  -0.013 0.989454    
stenose90-99%             -1.218e+01  8.827e+02  -0.014 0.988990    
stenose100% (Occlusion)   -1.160e+01  8.827e+02  -0.013 0.989516    
hsCRP_plasma              -3.983e-04  1.983e-03  -0.201 0.840842    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 290.48  on 278  degrees of freedom
Residual deviance: 252.63  on 260  degrees of freedom
AIC: 290.63

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.711076 
Standard error............: 0.193961 
Odds ratio (effect size)..: 2.036 
Lower 95% CI..............: 1.392 
Upper 95% CI..............: 2.978 
Z-value...................: 3.666076 
P-value...................: 0.0002463009 
Hosmer and Lemeshow r^2...: 0.13032 
Cox and Snell r^2.........: 0.12688 
Nagelkerke's pseudo r^2...: 0.19612 
Sample size of AE DB......: 2388 
Sample size of model......: 279 
Missing data %............: 88.31658 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)   Gendermale  
     0.5645       0.8471  

Degrees of Freedom: 278 Total (i.e. Null);  277 Residual
Null Deviance:      309.9 
Residual Deviance: 301.7    AIC: 305.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2386   0.2287   0.6171   0.7649   1.4284  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                13.273572 882.747406   0.015  0.98800   
currentDF[, PROTEIN]       -0.217854   0.178046  -1.224  0.22111   
Age                         0.008607   0.019930   0.432  0.66585   
Gendermale                  1.005564   0.323913   3.104  0.00191 **
Hypertension.compositeyes   0.085207   0.409705   0.208  0.83525   
DiabetesStatusDiabetes     -0.362545   0.386058  -0.939  0.34768   
SmokerCurrentyes            0.365995   0.325207   1.125  0.26041   
Med.Statin.LLDyes          -0.383866   0.348667  -1.101  0.27092   
Med.all.antiplateletyes     0.594389   0.542350   1.096  0.27310   
GFR_MDRD                   -0.001712   0.009224  -0.186  0.85273   
BMI                         0.015316   0.036457   0.420  0.67440   
CAD_history                 0.166401   0.346892   0.480  0.63145   
Stroke_history             -0.020672   0.331278  -0.062  0.95024   
Peripheral.interv           0.215540   0.383531   0.562  0.57412   
stenose50-70%             -12.581005 882.744345  -0.014  0.98863   
stenose70-90%             -13.252706 882.743558  -0.015  0.98802   
stenose90-99%             -13.155460 882.743548  -0.015  0.98811   
stenose100% (Occlusion)   -13.618488 882.744627  -0.015  0.98769   
hsCRP_plasma                0.003153   0.004361   0.723  0.46965   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 309.88  on 278  degrees of freedom
Residual deviance: 291.99  on 260  degrees of freedom
AIC: 329.99

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: IPH 
Effect size...............: -0.217854 
Standard error............: 0.178046 
Odds ratio (effect size)..: 0.804 
Lower 95% CI..............: 0.567 
Upper 95% CI..............: 1.14 
Z-value...................: -1.223586 
P-value...................: 0.2211086 
Hosmer and Lemeshow r^2...: 0.05772 
Cox and Snell r^2.........: 0.062097 
Nagelkerke's pseudo r^2...: 0.09259 
Sample size of AE DB......: 2388 
Sample size of model......: 279 
Missing data %............: 88.31658 

Analysis of IL6_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent + 
    CAD_history + Peripheral.interv + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)                      Age         SmokerCurrentyes              CAD_history        Peripheral.interv  
               -2.03767                  0.01906                  0.31759                  0.28534                 -0.39470  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose70-99%  
               -0.26332                  0.44676                  0.69678                  1.70987                -14.15643  

Degrees of Freedom: 613 Total (i.e. Null);  604 Residual
Null Deviance:      849.9 
Residual Deviance: 824.6    AIC: 844.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6386  -1.1238  -0.7819   1.1603   1.7127  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -3.173e+00  1.756e+00  -1.807   0.0708 .
currentDF[, PROTEIN]      -1.540e-02  5.886e-02  -0.262   0.7936  
Age                        2.147e-02  1.055e-02   2.034   0.0420 *
Gendermale                 3.077e-02  1.826e-01   0.169   0.8662  
Hypertension.compositeyes  2.945e-01  2.552e-01   1.154   0.2485  
DiabetesStatusDiabetes    -2.425e-02  2.117e-01  -0.115   0.9088  
SmokerCurrentyes           3.489e-01  1.844e-01   1.892   0.0585 .
Med.Statin.LLDyes         -2.639e-01  2.026e-01  -1.302   0.1928  
Med.all.antiplateletyes    6.589e-02  2.938e-01   0.224   0.8226  
GFR_MDRD                   4.745e-03  4.713e-03   1.007   0.3140  
BMI                        1.438e-02  2.247e-02   0.640   0.5220  
CAD_history                2.970e-01  1.954e-01   1.520   0.1286  
Stroke_history             5.443e-03  1.840e-01   0.030   0.9764  
Peripheral.interv         -3.830e-01  2.158e-01  -1.775   0.0759 .
stenose50-70%             -1.919e-01  1.300e+00  -0.148   0.8826  
stenose70-90%              5.145e-01  1.251e+00   0.411   0.6808  
stenose90-99%              7.534e-01  1.251e+00   0.602   0.5469  
stenose100% (Occlusion)    1.846e+00  1.719e+00   1.074   0.2827  
stenose70-99%             -1.407e+01  4.293e+02  -0.033   0.9739  
hsCRP_plasma              -3.988e-04  7.677e-04  -0.519   0.6034  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 849.91  on 613  degrees of freedom
Residual deviance: 819.86  on 594  degrees of freedom
AIC: 859.86

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.015399 
Standard error............: 0.05886 
Odds ratio (effect size)..: 0.985 
Lower 95% CI..............: 0.877 
Upper 95% CI..............: 1.105 
Z-value...................: -0.261629 
P-value...................: 0.7936074 
Hosmer and Lemeshow r^2...: 0.035358 
Cox and Snell r^2.........: 0.047765 
Nagelkerke's pseudo r^2...: 0.063731 
Sample size of AE DB......: 2388 
Sample size of model......: 614 
Missing data %............: 74.28811 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerCurrent + CAD_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]      SmokerCurrentyes           CAD_history  
              0.2877               -0.2105                0.4717                0.5078  

Degrees of Freedom: 616 Total (i.e. Null);  613 Residual
Null Deviance:      658.4 
Residual Deviance: 639.6    AIC: 647.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3103   0.3936   0.6280   0.7490   1.1420  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.236e+01  8.343e+02   0.015   0.9882   
currentDF[, PROTEIN]      -2.279e-01  7.376e-02  -3.090   0.0020 **
Age                        6.682e-03  1.232e-02   0.543   0.5874   
Gendermale                 7.361e-02  2.168e-01   0.340   0.7342   
Hypertension.compositeyes  1.575e-01  2.883e-01   0.546   0.5849   
DiabetesStatusDiabetes     2.084e-01  2.609e-01   0.799   0.4244   
SmokerCurrentyes           5.500e-01  2.293e-01   2.399   0.0165 * 
Med.Statin.LLDyes         -1.219e-01  2.420e-01  -0.504   0.6145   
Med.all.antiplateletyes    5.306e-01  3.297e-01   1.609   0.1075   
GFR_MDRD                   2.483e-03  5.598e-03   0.444   0.6574   
BMI                        2.501e-02  2.806e-02   0.891   0.3727   
CAD_history                5.373e-01  2.479e-01   2.167   0.0302 * 
Stroke_history             3.066e-01  2.238e-01   1.370   0.1707   
Peripheral.interv          1.005e-01  2.631e-01   0.382   0.7024   
stenose50-70%             -1.414e+01  8.343e+02  -0.017   0.9865   
stenose70-90%             -1.420e+01  8.343e+02  -0.017   0.9864   
stenose90-99%             -1.414e+01  8.343e+02  -0.017   0.9865   
stenose100% (Occlusion)    7.104e-01  1.070e+03   0.001   0.9995   
stenose70-99%             -1.442e+01  8.343e+02  -0.017   0.9862   
hsCRP_plasma              -7.973e-04  7.313e-04  -1.090   0.2756   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 658.36  on 616  degrees of freedom
Residual deviance: 627.52  on 597  degrees of freedom
AIC: 667.52

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.227904 
Standard error............: 0.073764 
Odds ratio (effect size)..: 0.796 
Lower 95% CI..............: 0.689 
Upper 95% CI..............: 0.92 
Z-value...................: -3.089637 
P-value...................: 0.00200401 
Hosmer and Lemeshow r^2...: 0.046845 
Cox and Snell r^2.........: 0.048756 
Nagelkerke's pseudo r^2...: 0.074327 
Sample size of AE DB......: 2388 
Sample size of model......: 617 
Missing data %............: 74.16248 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale     Peripheral.interv  
              1.8788                0.4179                0.8153               -0.4469  

Degrees of Freedom: 616 Total (i.e. Null);  613 Residual
Null Deviance:      743.2 
Residual Deviance: 680.3    AIC: 688.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2635  -1.0430   0.6076   0.8200   2.0560  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                14.839255 506.969431   0.029   0.9766    
currentDF[, PROTEIN]        0.414706   0.071877   5.770 7.94e-09 ***
Age                         0.006483   0.011992   0.541   0.5888    
Gendermale                  0.844744   0.201005   4.203 2.64e-05 ***
Hypertension.compositeyes   0.124301   0.286219   0.434   0.6641    
DiabetesStatusDiabetes     -0.036004   0.241122  -0.149   0.8813    
SmokerCurrentyes           -0.010489   0.210837  -0.050   0.9603    
Med.Statin.LLDyes          -0.055825   0.235469  -0.237   0.8126    
Med.all.antiplateletyes     0.006388   0.330784   0.019   0.9846    
GFR_MDRD                   -0.001176   0.005412  -0.217   0.8280    
BMI                         0.008795   0.024724   0.356   0.7220    
CAD_history                -0.052522   0.222360  -0.236   0.8133    
Stroke_history              0.225596   0.216332   1.043   0.2970    
Peripheral.interv          -0.462486   0.233665  -1.979   0.0478 *  
stenose50-70%             -14.082511 506.967579  -0.028   0.9778    
stenose70-90%             -13.716572 506.967461  -0.027   0.9784    
stenose90-99%             -13.606310 506.967460  -0.027   0.9786    
stenose100% (Occlusion)   -15.178060 506.968637  -0.030   0.9761    
stenose70-99%             -14.767795 506.968856  -0.029   0.9768    
hsCRP_plasma                0.000822   0.001299   0.633   0.5268    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 743.18  on 616  degrees of freedom
Residual deviance: 671.07  on 597  degrees of freedom
AIC: 711.07

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.414706 
Standard error............: 0.071877 
Odds ratio (effect size)..: 1.514 
Lower 95% CI..............: 1.315 
Upper 95% CI..............: 1.743 
Z-value...................: 5.769662 
P-value...................: 7.943079e-09 
Hosmer and Lemeshow r^2...: 0.097029 
Cox and Snell r^2.........: 0.110301 
Nagelkerke's pseudo r^2...: 0.157536 
Sample size of AE DB......: 2388 
Sample size of model......: 617 
Missing data %............: 74.16248 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)               Gendermale         SmokerCurrentyes        Med.Statin.LLDyes  Med.all.antiplateletyes  
                -0.2863                   0.6645                   0.4139                  -0.3598                   0.4213  

Degrees of Freedom: 615 Total (i.e. Null);  611 Residual
Null Deviance:      828.8 
Residual Deviance: 806.9    AIC: 816.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0797  -1.2434   0.8004   0.9968   1.5487  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                0.6679016  1.7675384   0.378 0.705526    
currentDF[, PROTEIN]       0.0853773  0.0607429   1.406 0.159857    
Age                       -0.0007668  0.0107421  -0.071 0.943095    
Gendermale                 0.7046627  0.1860325   3.788 0.000152 ***
Hypertension.compositeyes -0.3427477  0.2654362  -1.291 0.196613    
DiabetesStatusDiabetes    -0.1475819  0.2139819  -0.690 0.490387    
SmokerCurrentyes           0.3944830  0.1922973   2.051 0.040226 *  
Med.Statin.LLDyes         -0.3594273  0.2126356  -1.690 0.090962 .  
Med.all.antiplateletyes    0.3905767  0.2954866   1.322 0.186232    
GFR_MDRD                  -0.0050159  0.0048312  -1.038 0.299158    
BMI                        0.0091376  0.0228338   0.400 0.689024    
CAD_history                0.2170375  0.2011279   1.079 0.280542    
Stroke_history             0.2148625  0.1908222   1.126 0.260173    
Peripheral.interv          0.0992787  0.2211831   0.449 0.653538    
stenose50-70%             -0.7347676  1.2830633  -0.573 0.566870    
stenose70-90%             -0.4118810  1.2418707  -0.332 0.740145    
stenose90-99%             -0.1347980  1.2418505  -0.109 0.913563    
stenose100% (Occlusion)   -0.9043600  1.6034061  -0.564 0.572738    
stenose70-99%              0.3900736  1.7017441   0.229 0.818698    
hsCRP_plasma              -0.0006036  0.0007987  -0.756 0.449817    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 828.82  on 615  degrees of freedom
Residual deviance: 794.18  on 596  degrees of freedom
AIC: 834.18

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: 0.085377 
Standard error............: 0.060743 
Odds ratio (effect size)..: 1.089 
Lower 95% CI..............: 0.967 
Upper 95% CI..............: 1.227 
Z-value...................: 1.405553 
P-value...................: 0.1598569 
Hosmer and Lemeshow r^2...: 0.041805 
Cox and Snell r^2.........: 0.054696 
Nagelkerke's pseudo r^2...: 0.073954 
Sample size of AE DB......: 2388 
Sample size of model......: 616 
Missing data %............: 74.20436 

Analysis of IL6R_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent + 
    CAD_history + Peripheral.interv + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)                      Age         SmokerCurrentyes              CAD_history        Peripheral.interv  
               -1.39761                  0.01805                  0.26766                  0.28298                 -0.44359  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%  
               -0.76341                 -0.08234                  0.14517                  1.15737                -15.67282  
          stenose70-99%  
              -15.68045  

Degrees of Freedom: 618 Total (i.e. Null);  608 Residual
Null Deviance:      856.9 
Residual Deviance: 830.8    AIC: 852.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.5341  -1.1333  -0.7798   1.1685   1.7335  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -2.304e+00  1.868e+00  -1.233   0.2174  
currentDF[, PROTEIN]      -2.550e-02  7.191e-02  -0.355   0.7229  
Age                        2.039e-02  1.051e-02   1.939   0.0525 .
Gendermale                -8.156e-03  1.812e-01  -0.045   0.9641  
Hypertension.compositeyes  2.760e-01  2.520e-01   1.095   0.2734  
DiabetesStatusDiabetes    -1.014e-01  2.113e-01  -0.480   0.6314  
SmokerCurrentyes           2.921e-01  1.829e-01   1.597   0.1103  
Med.Statin.LLDyes         -1.654e-01  2.047e-01  -0.808   0.4190  
Med.all.antiplateletyes    1.379e-01  2.936e-01   0.470   0.6386  
GFR_MDRD                   4.026e-03  4.683e-03   0.860   0.3899  
BMI                        3.436e-03  2.296e-02   0.150   0.8810  
CAD_history                2.917e-01  1.947e-01   1.498   0.1340  
Stroke_history            -1.209e-02  1.823e-01  -0.066   0.9471  
Peripheral.interv         -4.342e-01  2.163e-01  -2.007   0.0447 *
stenose50-70%             -6.652e-01  1.476e+00  -0.451   0.6523  
stenose70-90%              2.555e-02  1.434e+00   0.018   0.9858  
stenose90-99%              2.454e-01  1.436e+00   0.171   0.8643  
stenose100% (Occlusion)    1.308e+00  1.854e+00   0.706   0.4803  
stenose50-99%             -1.566e+01  1.029e+03  -0.015   0.9879  
stenose70-99%             -1.564e+01  7.120e+02  -0.022   0.9825  
hsCRP_plasma              -4.107e-04  7.569e-04  -0.543   0.5874  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 856.94  on 618  degrees of freedom
Residual deviance: 827.41  on 598  degrees of freedom
AIC: 869.41

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.025502 
Standard error............: 0.071908 
Odds ratio (effect size)..: 0.975 
Lower 95% CI..............: 0.847 
Upper 95% CI..............: 1.122 
Z-value...................: -0.354646 
P-value...................: 0.7228549 
Hosmer and Lemeshow r^2...: 0.034457 
Cox and Snell r^2.........: 0.046582 
Nagelkerke's pseudo r^2...: 0.062149 
Sample size of AE DB......: 2388 
Sample size of model......: 619 
Missing data %............: 74.07873 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent + 
    Med.all.antiplatelet + CAD_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)         SmokerCurrentyes  Med.all.antiplateletyes              CAD_history  
                 0.4945                   0.4512                   0.4820                   0.5581  

Degrees of Freedom: 621 Total (i.e. Null);  618 Residual
Null Deviance:      665.8 
Residual Deviance: 653.9    AIC: 661.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2601   0.4421   0.6377   0.7667   1.1349  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.310e+01  1.011e+03   0.013   0.9897  
currentDF[, PROTEIN]       2.674e-03  8.620e-02   0.031   0.9753  
Age                        9.604e-03  1.224e-02   0.784   0.4328  
Gendermale                 1.461e-01  2.117e-01   0.690   0.4902  
Hypertension.compositeyes  1.037e-01  2.844e-01   0.365   0.7153  
DiabetesStatusDiabetes     2.294e-01  2.589e-01   0.886   0.3755  
SmokerCurrentyes           5.379e-01  2.259e-01   2.381   0.0173 *
Med.Statin.LLDyes         -2.637e-02  2.395e-01  -0.110   0.9123  
Med.all.antiplateletyes    5.600e-01  3.220e-01   1.739   0.0820 .
GFR_MDRD                   1.793e-03  5.543e-03   0.323   0.7464  
BMI                        2.298e-02  2.773e-02   0.829   0.4072  
CAD_history                5.430e-01  2.441e-01   2.224   0.0261 *
Stroke_history             1.959e-01  2.191e-01   0.894   0.3713  
Peripheral.interv          1.392e-01  2.614e-01   0.532   0.5944  
stenose50-70%             -1.431e+01  1.011e+03  -0.014   0.9887  
stenose70-90%             -1.446e+01  1.011e+03  -0.014   0.9886  
stenose90-99%             -1.431e+01  1.011e+03  -0.014   0.9887  
stenose100% (Occlusion)    3.082e-01  1.229e+03   0.000   0.9998  
stenose50-99%             -3.990e-01  1.442e+03   0.000   0.9998  
stenose70-99%             -1.468e+01  1.011e+03  -0.015   0.9884  
hsCRP_plasma              -8.408e-04  7.371e-04  -1.141   0.2540  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 665.84  on 621  degrees of freedom
Residual deviance: 643.59  on 601  degrees of freedom
AIC: 685.59

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.002674 
Standard error............: 0.086197 
Odds ratio (effect size)..: 1.003 
Lower 95% CI..............: 0.847 
Upper 95% CI..............: 1.187 
Z-value...................: 0.031024 
P-value...................: 0.9752504 
Hosmer and Lemeshow r^2...: 0.033428 
Cox and Snell r^2.........: 0.035151 
Nagelkerke's pseudo r^2...: 0.05349 
Sample size of AE DB......: 2388 
Sample size of model......: 622 
Missing data %............: 73.9531 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale        Stroke_history     Peripheral.interv  
              0.7423                0.2002                0.8589                0.3515               -0.4777  

Degrees of Freedom: 621 Total (i.e. Null);  617 Residual
Null Deviance:      750.2 
Residual Deviance: 714.1    AIC: 724.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0759  -1.1783   0.6695   0.8326   1.7892  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.378e+01  6.241e+02   0.022   0.9824    
currentDF[, PROTEIN]       1.982e-01  7.742e-02   2.561   0.0105 *  
Age                        9.299e-03  1.162e-02   0.800   0.4237    
Gendermale                 8.983e-01  1.954e-01   4.597 4.28e-06 ***
Hypertension.compositeyes  1.373e-01  2.760e-01   0.498   0.6188    
DiabetesStatusDiabetes    -6.609e-03  2.343e-01  -0.028   0.9775    
SmokerCurrentyes           6.358e-02  2.053e-01   0.310   0.7568    
Med.Statin.LLDyes          4.918e-02  2.321e-01   0.212   0.8322    
Med.all.antiplateletyes    5.701e-02  3.244e-01   0.176   0.8605    
GFR_MDRD                  -2.183e-04  5.287e-03  -0.041   0.9671    
BMI                       -3.735e-03  2.513e-02  -0.149   0.8818    
CAD_history               -1.611e-01  2.163e-01  -0.745   0.4563    
Stroke_history             3.315e-01  2.104e-01   1.575   0.1152    
Peripheral.interv         -4.879e-01  2.286e-01  -2.135   0.0328 *  
stenose50-70%             -1.408e+01  6.241e+02  -0.023   0.9820    
stenose70-90%             -1.372e+01  6.241e+02  -0.022   0.9825    
stenose90-99%             -1.375e+01  6.241e+02  -0.022   0.9824    
stenose100% (Occlusion)   -1.502e+01  6.241e+02  -0.024   0.9808    
stenose50-99%             -2.931e+01  8.788e+02  -0.033   0.9734    
stenose70-99%             -1.442e+01  6.241e+02  -0.023   0.9816    
hsCRP_plasma               1.043e-03  1.179e-03   0.884   0.3766    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 750.18  on 621  degrees of freedom
Residual deviance: 702.43  on 601  degrees of freedom
AIC: 744.43

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.198232 
Standard error............: 0.077419 
Odds ratio (effect size)..: 1.219 
Lower 95% CI..............: 1.048 
Upper 95% CI..............: 1.419 
Z-value...................: 2.560522 
P-value...................: 0.01045151 
Hosmer and Lemeshow r^2...: 0.063657 
Cox and Snell r^2.........: 0.073903 
Nagelkerke's pseudo r^2...: 0.10548 
Sample size of AE DB......: 2388 
Sample size of model......: 622 
Missing data %............: 73.9531 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + SmokerCurrent, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale      SmokerCurrentyes  
              0.2477                0.1965                0.6427                0.3405  

Degrees of Freedom: 619 Total (i.e. Null);  616 Residual
Null Deviance:      835.3 
Residual Deviance: 812.2    AIC: 820.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9967  -1.2590   0.7936   0.9869   1.6855  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                8.507e-01  1.890e+00   0.450 0.652583    
currentDF[, PROTEIN]       1.700e-01  7.341e-02   2.316 0.020566 *  
Age                       -3.382e-03  1.076e-02  -0.314 0.753214    
Gendermale                 6.921e-01  1.847e-01   3.748 0.000178 ***
Hypertension.compositeyes -2.837e-01  2.610e-01  -1.087 0.277074    
DiabetesStatusDiabetes    -1.516e-01  2.132e-01  -0.711 0.477112    
SmokerCurrentyes           3.079e-01  1.906e-01   1.616 0.106170    
Med.Statin.LLDyes         -2.823e-01  2.140e-01  -1.319 0.187174    
Med.all.antiplateletyes    3.726e-01  2.944e-01   1.265 0.205711    
GFR_MDRD                  -5.088e-03  4.824e-03  -1.055 0.291601    
BMI                       -6.471e-03  2.342e-02  -0.276 0.782284    
CAD_history                1.857e-01  2.002e-01   0.928 0.353513    
Stroke_history             1.617e-01  1.891e-01   0.855 0.392598    
Peripheral.interv          5.488e-02  2.211e-01   0.248 0.803927    
stenose50-70%             -2.050e-01  1.476e+00  -0.139 0.889554    
stenose70-90%              8.601e-02  1.439e+00   0.060 0.952351    
stenose90-99%              2.269e-01  1.441e+00   0.157 0.874921    
stenose100% (Occlusion)   -3.913e-01  1.763e+00  -0.222 0.824387    
stenose50-99%              1.452e+01  6.222e+02   0.023 0.981376    
stenose70-99%              1.153e+00  1.848e+00   0.624 0.532790    
hsCRP_plasma              -5.008e-04  7.982e-04  -0.627 0.530421    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 835.34  on 619  degrees of freedom
Residual deviance: 799.72  on 599  degrees of freedom
AIC: 841.72

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: 0.170015 
Standard error............: 0.073413 
Odds ratio (effect size)..: 1.185 
Lower 95% CI..............: 1.026 
Upper 95% CI..............: 1.369 
Z-value...................: 2.315862 
P-value...................: 0.0205658 
Hosmer and Lemeshow r^2...: 0.042645 
Cox and Snell r^2.........: 0.055838 
Nagelkerke's pseudo r^2...: 0.07545 
Sample size of AE DB......: 2388 
Sample size of model......: 620 
Missing data %............: 74.03685 

Analysis of MCP1_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + SmokerCurrent + Peripheral.interv + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age         SmokerCurrentyes        Peripheral.interv  
               -2.91466                 -0.34810                  0.02251                  0.27168                 -0.41190  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%  
                0.15264                  0.84843                  0.97712                  1.65836                -14.89486  
          stenose70-99%  
              -14.54448  

Degrees of Freedom: 637 Total (i.e. Null);  627 Residual
Null Deviance:      882.6 
Residual Deviance: 828.1    AIC: 850.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7097  -1.0741  -0.6643   1.1304   1.9192  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -3.674e+00  1.768e+00  -2.078   0.0377 *  
currentDF[, PROTEIN]      -3.538e-01  6.884e-02  -5.139 2.76e-07 ***
Age                        2.253e-02  1.063e-02   2.118   0.0341 *  
Gendermale                 4.587e-02  1.831e-01   0.250   0.8022    
Hypertension.compositeyes  2.489e-01  2.549e-01   0.976   0.3289    
DiabetesStatusDiabetes    -1.151e-01  2.131e-01  -0.540   0.5890    
SmokerCurrentyes           2.944e-01  1.843e-01   1.597   0.1102    
Med.Statin.LLDyes         -2.974e-01  2.054e-01  -1.448   0.1476    
Med.all.antiplateletyes    1.180e-01  2.931e-01   0.403   0.6873    
GFR_MDRD                   4.104e-03  4.750e-03   0.864   0.3876    
BMI                        8.952e-03  2.250e-02   0.398   0.6907    
CAD_history                2.608e-01  1.947e-01   1.340   0.1803    
Stroke_history             1.081e-01  1.845e-01   0.586   0.5580    
Peripheral.interv         -4.220e-01  2.168e-01  -1.946   0.0516 .  
stenose50-70%              1.388e-01  1.300e+00   0.107   0.9150    
stenose70-90%              8.620e-01  1.250e+00   0.690   0.4905    
stenose90-99%              9.815e-01  1.249e+00   0.786   0.4321    
stenose100% (Occlusion)    1.669e+00  1.720e+00   0.970   0.3320    
stenose50-99%             -1.487e+01  9.913e+02  -0.015   0.9880    
stenose70-99%             -1.452e+01  6.892e+02  -0.021   0.9832    
hsCRP_plasma              -1.977e-04  8.180e-04  -0.242   0.8090    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 882.64  on 637  degrees of freedom
Residual deviance: 821.90  on 617  degrees of freedom
AIC: 863.9

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.353797 
Standard error............: 0.068844 
Odds ratio (effect size)..: 0.702 
Lower 95% CI..............: 0.613 
Upper 95% CI..............: 0.803 
Z-value...................: -5.139142 
P-value...................: 2.759964e-07 
Hosmer and Lemeshow r^2...: 0.068824 
Cox and Snell r^2.........: 0.090823 
Nagelkerke's pseudo r^2...: 0.121212 
Sample size of AE DB......: 2388 
Sample size of model......: 638 
Missing data %............: 73.28308 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerCurrent + CAD_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]      SmokerCurrentyes           CAD_history  
              0.7189               -0.2213                0.4743                0.5184  

Degrees of Freedom: 640 Total (i.e. Null);  637 Residual
Null Deviance:      683 
Residual Deviance: 664.1    AIC: 672.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3057   0.3895   0.6294   0.7514   1.1846  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.277e+01  8.213e+02   0.016  0.98760   
currentDF[, PROTEIN]      -2.100e-01  8.055e-02  -2.607  0.00914 **
Age                        7.341e-03  1.215e-02   0.604  0.54564   
Gendermale                 1.629e-01  2.112e-01   0.771  0.44070   
Hypertension.compositeyes  6.702e-02  2.849e-01   0.235  0.81405   
DiabetesStatusDiabetes     1.041e-01  2.531e-01   0.411  0.68101   
SmokerCurrentyes           5.453e-01  2.254e-01   2.420  0.01554 * 
Med.Statin.LLDyes         -7.761e-02  2.380e-01  -0.326  0.74441   
Med.all.antiplateletyes    4.811e-01  3.205e-01   1.501  0.13327   
GFR_MDRD                   2.145e-03  5.486e-03   0.391  0.69574   
BMI                        3.022e-02  2.743e-02   1.102  0.27053   
CAD_history                5.250e-01  2.409e-01   2.179  0.02933 * 
Stroke_history             2.599e-01  2.187e-01   1.188  0.23471   
Peripheral.interv          1.935e-01  2.606e-01   0.742  0.45781   
stenose50-70%             -1.407e+01  8.213e+02  -0.017  0.98633   
stenose70-90%             -1.423e+01  8.213e+02  -0.017  0.98618   
stenose90-99%             -1.416e+01  8.213e+02  -0.017  0.98625   
stenose100% (Occlusion)    1.434e-01  1.082e+03   0.000  0.99989   
stenose50-99%             -1.897e-01  1.312e+03   0.000  0.99988   
stenose70-99%             -1.428e+01  8.213e+02  -0.017  0.98613   
hsCRP_plasma              -7.496e-04  7.473e-04  -1.003  0.31581   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 682.96  on 640  degrees of freedom
Residual deviance: 652.30  on 620  degrees of freedom
AIC: 694.3

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.209978 
Standard error............: 0.080548 
Odds ratio (effect size)..: 0.811 
Lower 95% CI..............: 0.692 
Upper 95% CI..............: 0.949 
Z-value...................: -2.606859 
P-value...................: 0.009137705 
Hosmer and Lemeshow r^2...: 0.044886 
Cox and Snell r^2.........: 0.046699 
Nagelkerke's pseudo r^2...: 0.071249 
Sample size of AE DB......: 2388 
Sample size of model......: 641 
Missing data %............: 73.15745 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale        Stroke_history     Peripheral.interv  
              0.4900                0.1761                0.8581                0.3303               -0.3647  

Degrees of Freedom: 640 Total (i.e. Null);  636 Residual
Null Deviance:      779.2 
Residual Deviance: 742.6    AIC: 752.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0483  -1.1799   0.6663   0.8380   1.5720  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.446e+01  8.352e+02   0.017   0.9862    
currentDF[, PROTEIN]       1.835e-01  7.144e-02   2.569   0.0102 *  
Age                        6.300e-03  1.139e-02   0.553   0.5802    
Gendermale                 8.984e-01  1.914e-01   4.695 2.67e-06 ***
Hypertension.compositeyes  1.616e-01  2.721e-01   0.594   0.5527    
DiabetesStatusDiabetes    -4.434e-02  2.286e-01  -0.194   0.8462    
SmokerCurrentyes           7.693e-02  2.003e-01   0.384   0.7009    
Med.Statin.LLDyes         -7.305e-02  2.281e-01  -0.320   0.7487    
Med.all.antiplateletyes    9.405e-02  3.169e-01   0.297   0.7666    
GFR_MDRD                  -1.751e-04  5.212e-03  -0.034   0.9732    
BMI                        5.013e-03  2.372e-02   0.211   0.8326    
CAD_history               -1.538e-01  2.096e-01  -0.734   0.4632    
Stroke_history             3.187e-01  2.076e-01   1.535   0.1247    
Peripheral.interv         -3.990e-01  2.240e-01  -1.781   0.0749 .  
stenose50-70%             -1.511e+01  8.352e+02  -0.018   0.9856    
stenose70-90%             -1.467e+01  8.352e+02  -0.018   0.9860    
stenose90-99%             -1.458e+01  8.352e+02  -0.017   0.9861    
stenose100% (Occlusion)   -1.570e+01  8.352e+02  -0.019   0.9850    
stenose50-99%             -3.123e+01  1.301e+03  -0.024   0.9808    
stenose70-99%             -1.572e+01  8.352e+02  -0.019   0.9850    
hsCRP_plasma               9.362e-04  1.297e-03   0.722   0.4704    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 779.19  on 640  degrees of freedom
Residual deviance: 729.13  on 620  degrees of freedom
AIC: 771.13

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.183542 
Standard error............: 0.071439 
Odds ratio (effect size)..: 1.201 
Lower 95% CI..............: 1.044 
Upper 95% CI..............: 1.382 
Z-value...................: 2.569224 
P-value...................: 0.01019266 
Hosmer and Lemeshow r^2...: 0.064242 
Cox and Snell r^2.........: 0.075121 
Nagelkerke's pseudo r^2...: 0.106787 
Sample size of AE DB......: 2388 
Sample size of model......: 641 
Missing data %............: 73.15745 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)               Gendermale         SmokerCurrentyes        Med.Statin.LLDyes  Med.all.antiplateletyes  
                -0.2358                   0.6317                   0.4079                  -0.4060                   0.4192  

Degrees of Freedom: 638 Total (i.e. Null);  634 Residual
Null Deviance:      861.2 
Residual Deviance: 838.9    AIC: 848.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9939  -1.2484   0.7941   1.0068   1.5323  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                7.965e-01  1.749e+00   0.455 0.648904    
currentDF[, PROTEIN]      -4.119e-02  6.612e-02  -0.623 0.533296    
Age                       -4.261e-03  1.057e-02  -0.403 0.686765    
Gendermale                 6.900e-01  1.822e-01   3.786 0.000153 ***
Hypertension.compositeyes -3.421e-01  2.594e-01  -1.319 0.187182    
DiabetesStatusDiabetes    -1.849e-01  2.087e-01  -0.886 0.375694    
SmokerCurrentyes           3.607e-01  1.870e-01   1.928 0.053803 .  
Med.Statin.LLDyes         -4.409e-01  2.115e-01  -2.085 0.037101 *  
Med.all.antiplateletyes    3.845e-01  2.892e-01   1.329 0.183695    
GFR_MDRD                  -5.883e-03  4.757e-03  -1.237 0.216236    
BMI                        3.063e-03  2.235e-02   0.137 0.891022    
CAD_history                1.915e-01  1.950e-01   0.982 0.325976    
Stroke_history             2.583e-01  1.877e-01   1.376 0.168780    
Peripheral.interv          1.456e-01  2.168e-01   0.672 0.501897    
stenose50-70%             -6.460e-01  1.287e+00  -0.502 0.615744    
stenose70-90%             -2.998e-01  1.246e+00  -0.241 0.809789    
stenose90-99%             -1.076e-01  1.245e+00  -0.086 0.931122    
stenose100% (Occlusion)   -8.523e-01  1.608e+00  -0.530 0.596138    
stenose50-99%              1.413e+01  6.238e+02   0.023 0.981927    
stenose70-99%              5.240e-01  1.702e+00   0.308 0.758158    
hsCRP_plasma              -5.350e-04  7.957e-04  -0.672 0.501368    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 861.23  on 638  degrees of freedom
Residual deviance: 825.85  on 618  degrees of freedom
AIC: 867.85

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: -0.041195 
Standard error............: 0.066125 
Odds ratio (effect size)..: 0.96 
Lower 95% CI..............: 0.843 
Upper 95% CI..............: 1.092 
Z-value...................: -0.622983 
P-value...................: 0.5332958 
Hosmer and Lemeshow r^2...: 0.041078 
Cox and Snell r^2.........: 0.053859 
Nagelkerke's pseudo r^2...: 0.072765 
Sample size of AE DB......: 2388 
Sample size of model......: 639 
Missing data %............: 73.24121 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    DiabetesStatus + Med.Statin.LLD + CAD_history, data = currentDF)

Coefficients:
           (Intercept)      currentDF[, TRAIT]                     Age  DiabetesStatusDiabetes       Med.Statin.LLDyes  
               1.10461                -0.08253                -0.01480                -0.30374                -0.25618  
           CAD_history  
               0.25223  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.8179 -0.7134 -0.0181  0.6918  2.5356 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                3.7610801  1.3489297   2.788  0.00571 **
currentDF[, TRAIT]        -0.0850507  0.0553685  -1.536  0.12579   
Age                       -0.0141240  0.0078693  -1.795  0.07389 . 
Gendermale                -0.0741715  0.1340836  -0.553  0.58064   
Hypertension.compositeyes  0.0013148  0.1710049   0.008  0.99387   
DiabetesStatusDiabetes    -0.2388949  0.1703963  -1.402  0.16216   
SmokerCurrentyes           0.0345861  0.1281135   0.270  0.78741   
Med.Statin.LLDyes         -0.2708574  0.1319532  -2.053  0.04115 * 
Med.all.antiplateletyes   -0.1694608  0.2416126  -0.701  0.48372   
GFR_MDRD                   0.0031164  0.0036469   0.855  0.39364   
BMI                       -0.0127276  0.0159841  -0.796  0.42663   
CAD_history                0.3022280  0.1347847   2.242  0.02582 * 
Stroke_history             0.0493347  0.1319419   0.374  0.70879   
Peripheral.interv         -0.0289171  0.1476817  -0.196  0.84492   
stenose50-70%             -2.6729158  1.0737710  -2.489  0.01345 * 
stenose70-90%             -2.3552407  0.9731508  -2.420  0.01623 * 
stenose90-99%             -2.4519655  0.9711424  -2.525  0.01220 * 
stenose100% (Occlusion)   -2.8741799  1.0913091  -2.634  0.00897 **
hsCRP_plasma               0.0004683  0.0006729   0.696  0.48715   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9476 on 249 degrees of freedom
Multiple R-squared:  0.09334,   Adjusted R-squared:  0.0278 
F-statistic: 1.424 on 18 and 249 DF,  p-value: 0.1203

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: -0.085051 
Standard error............: 0.055368 
Odds ratio (effect size)..: 0.918 
Lower 95% CI..............: 0.824 
Upper 95% CI..............: 1.024 
T-value...................: -1.536086 
P-value...................: 0.1257871 
R^2.......................: 0.093337 
Adjusted r^2..............: 0.027795 
Sample size of AE DB......: 2388 
Sample size of model......: 268 
Missing data %............: 88.77722 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + DiabetesStatus + Med.Statin.LLD + 
    CAD_history, data = currentDF)

Coefficients:
           (Intercept)                     Age  DiabetesStatusDiabetes       Med.Statin.LLDyes             CAD_history  
               1.12857                -0.01497                -0.30012                -0.24553                 0.21063  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-1.9058 -0.6863 -0.0012  0.7162  2.5728 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                3.7574126  1.3650393   2.753  0.00635 **
currentDF[, TRAIT]         0.0291045  0.0602489   0.483  0.62947   
Age                       -0.0142636  0.0080214  -1.778  0.07659 . 
Gendermale                -0.0575822  0.1363481  -0.422  0.67316   
Hypertension.compositeyes -0.0416310  0.1714500  -0.243  0.80835   
DiabetesStatusDiabetes    -0.2241496  0.1718816  -1.304  0.19341   
SmokerCurrentyes           0.0435941  0.1284336   0.339  0.73457   
Med.Statin.LLDyes         -0.2565874  0.1344193  -1.909  0.05743 . 
Med.all.antiplateletyes   -0.1217945  0.2435305  -0.500  0.61743   
GFR_MDRD                   0.0027549  0.0037036   0.744  0.45767   
BMI                       -0.0163280  0.0161338  -1.012  0.31250   
CAD_history                0.2534281  0.1364387   1.857  0.06443 . 
Stroke_history             0.0387521  0.1340692   0.289  0.77279   
Peripheral.interv          0.0138521  0.1499281   0.092  0.92646   
stenose50-70%             -2.6358961  1.0930424  -2.412  0.01661 * 
stenose70-90%             -2.2673760  0.9862332  -2.299  0.02233 * 
stenose90-99%             -2.3358513  0.9830834  -2.376  0.01826 * 
stenose100% (Occlusion)   -2.7347521  1.1035886  -2.478  0.01387 * 
hsCRP_plasma               0.0005594  0.0006783   0.825  0.41032   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9598 on 249 degrees of freedom
Multiple R-squared:  0.08181,   Adjusted R-squared:  0.01543 
F-statistic: 1.233 on 18 and 249 DF,  p-value: 0.2353

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: 0.029104 
Standard error............: 0.060249 
Odds ratio (effect size)..: 1.03 
Lower 95% CI..............: 0.915 
Upper 95% CI..............: 1.159 
T-value...................: 0.48307 
P-value...................: 0.6294703 
R^2.......................: 0.08181 
Adjusted r^2..............: 0.015435 
Sample size of AE DB......: 2388 
Sample size of model......: 268 
Missing data %............: 88.77722 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + DiabetesStatus + Med.Statin.LLD + 
    CAD_history, data = currentDF)

Coefficients:
           (Intercept)                     Age  DiabetesStatusDiabetes       Med.Statin.LLDyes             CAD_history  
               1.24577                -0.01694                -0.29696                -0.22735                 0.20814  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.91883 -0.73434 -0.02321  0.71205  2.41508 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                3.9391587  1.3789460   2.857  0.00465 **
currentDF[, TRAIT]        -0.1104053  0.0755416  -1.462  0.14516   
Age                       -0.0163042  0.0080494  -2.026  0.04390 * 
Gendermale                -0.0309865  0.1359232  -0.228  0.81986   
Hypertension.compositeyes  0.0159954  0.1748906   0.091  0.92720   
DiabetesStatusDiabetes    -0.2229010  0.1732834  -1.286  0.19954   
SmokerCurrentyes           0.0548484  0.1288247   0.426  0.67066   
Med.Statin.LLDyes         -0.2373739  0.1340207  -1.771  0.07778 . 
Med.all.antiplateletyes   -0.1495074  0.2468978  -0.606  0.54538   
GFR_MDRD                   0.0030589  0.0037484   0.816  0.41528   
BMI                       -0.0153285  0.0161300  -0.950  0.34289   
CAD_history                0.2531491  0.1360555   1.861  0.06400 . 
Stroke_history             0.0566228  0.1338465   0.423  0.67264   
Peripheral.interv         -0.0107785  0.1500217  -0.072  0.94278   
stenose50-70%             -2.7403356  1.0878828  -2.519  0.01241 * 
stenose70-90%             -2.4021458  0.9863600  -2.435  0.01559 * 
stenose90-99%             -2.4746008  0.9837156  -2.516  0.01253 * 
stenose100% (Occlusion)   -2.6850845  1.1429843  -2.349  0.01961 * 
hsCRP_plasma               0.0006186  0.0006768   0.914  0.36162   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9561 on 244 degrees of freedom
Multiple R-squared:  0.0901,    Adjusted R-squared:  0.02298 
F-statistic: 1.342 on 18 and 244 DF,  p-value: 0.1622

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.110405 
Standard error............: 0.075542 
Odds ratio (effect size)..: 0.895 
Lower 95% CI..............: 0.772 
Upper 95% CI..............: 1.038 
T-value...................: -1.461517 
P-value...................: 0.1451602 
R^2.......................: 0.090105 
Adjusted r^2..............: 0.022981 
Sample size of AE DB......: 2388 
Sample size of model......: 263 
Missing data %............: 88.9866 

Analysis of MCP1_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + Med.Statin.LLD + Med.all.antiplatelet, 
    data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale  Hypertension.compositeyes  
                  0.90896                    0.09036                   -0.01335                    0.46652                   -0.40919  
        Med.Statin.LLDyes    Med.all.antiplateletyes  
                 -0.28685                    0.33829  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.64392 -0.75932  0.06352  0.66219  2.52268 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                2.6665896  1.4253886   1.871  0.06249 . 
currentDF[, TRAIT]         0.0725218  0.0582033   1.246  0.21387   
Age                       -0.0182822  0.0082423  -2.218  0.02740 * 
Gendermale                 0.4529019  0.1398723   3.238  0.00136 **
Hypertension.compositeyes -0.4589879  0.1783022  -2.574  0.01059 * 
DiabetesStatusDiabetes    -0.2271631  0.1698214  -1.338  0.18216   
SmokerCurrentyes          -0.0645039  0.1356126  -0.476  0.63472   
Med.Statin.LLDyes         -0.2770796  0.1404174  -1.973  0.04951 * 
Med.all.antiplateletyes    0.2477201  0.2397855   1.033  0.30251   
GFR_MDRD                  -0.0023312  0.0037741  -0.618  0.53731   
BMI                       -0.0058342  0.0158908  -0.367  0.71381   
CAD_history                0.1504213  0.1426425   1.055  0.29261   
Stroke_history             0.0147719  0.1374410   0.107  0.91449   
Peripheral.interv         -0.1156181  0.1590149  -0.727  0.46782   
stenose50-70%             -1.1952274  1.1480343  -1.041  0.29878   
stenose70-90%             -0.8922860  1.0461758  -0.853  0.39449   
stenose90-99%             -0.9612964  1.0452526  -0.920  0.35858   
stenose100% (Occlusion)   -2.0375086  1.2127086  -1.680  0.09412 . 
hsCRP_plasma               0.0002384  0.0007237   0.329  0.74215   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.021 on 263 degrees of freedom
Multiple R-squared:  0.1195,    Adjusted R-squared:  0.05923 
F-statistic: 1.983 on 18 and 263 DF,  p-value: 0.01102

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.072522 
Standard error............: 0.058203 
Odds ratio (effect size)..: 1.075 
Lower 95% CI..............: 0.959 
Upper 95% CI..............: 1.205 
T-value...................: 1.246007 
P-value...................: 0.2138706 
R^2.......................: 0.119492 
Adjusted r^2..............: 0.059229 
Sample size of AE DB......: 2388 
Sample size of model......: 282 
Missing data %............: 88.19096 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + Med.Statin.LLD, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale  Hypertension.compositeyes  
                  1.42056                   -0.15786                   -0.01593                    0.43251                   -0.37321  
        Med.Statin.LLDyes  
                 -0.27687  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.62938 -0.73219  0.02213  0.64926  2.37564 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                2.7696836  1.4071347   1.968  0.05008 . 
currentDF[, TRAIT]        -0.1596645  0.0608963  -2.622  0.00925 **
Age                       -0.0204731  0.0082046  -2.495  0.01320 * 
Gendermale                 0.4070673  0.1387248   2.934  0.00364 **
Hypertension.compositeyes -0.4316998  0.1745410  -2.473  0.01402 * 
DiabetesStatusDiabetes    -0.1996904  0.1681466  -1.188  0.23606   
SmokerCurrentyes          -0.0737715  0.1324886  -0.557  0.57813   
Med.Statin.LLDyes         -0.2893915  0.1398711  -2.069  0.03952 * 
Med.all.antiplateletyes    0.2047848  0.2362858   0.867  0.38691   
GFR_MDRD                  -0.0015786  0.0037332  -0.423  0.67274   
BMI                       -0.0026959  0.0157030  -0.172  0.86382   
CAD_history                0.2159653  0.1407712   1.534  0.12619   
Stroke_history             0.0502527  0.1362064   0.369  0.71247   
Peripheral.interv         -0.1498806  0.1578178  -0.950  0.34313   
stenose50-70%             -1.1018731  1.1377402  -0.968  0.33370   
stenose70-90%             -0.9048915  1.0354045  -0.874  0.38294   
stenose90-99%             -0.9856399  1.0334683  -0.954  0.34110   
stenose100% (Occlusion)   -2.1331283  1.1977774  -1.781  0.07608 . 
hsCRP_plasma               0.0001592  0.0007124   0.223  0.82333   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.01 on 263 degrees of freedom
Multiple R-squared:  0.1323,    Adjusted R-squared:  0.0729 
F-statistic: 2.227 on 18 and 263 DF,  p-value: 0.003394

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.159664 
Standard error............: 0.060896 
Odds ratio (effect size)..: 0.852 
Lower 95% CI..............: 0.757 
Upper 95% CI..............: 0.96 
T-value...................: -2.621908 
P-value...................: 0.009253515 
R^2.......................: 0.132285 
Adjusted r^2..............: 0.072898 
Sample size of AE DB......: 2388 
Sample size of model......: 282 
Missing data %............: 88.19096 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + Hypertension.composite + 
    Med.Statin.LLD, data = currentDF)

Coefficients:
              (Intercept)                        Age                 Gendermale  Hypertension.compositeyes          Med.Statin.LLDyes  
                  1.22370                   -0.01415                    0.48743                   -0.34890                   -0.28148  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.65420 -0.71370  0.04394  0.68616  2.62817 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                2.7729405  1.4437166   1.921  0.05587 . 
currentDF[, TRAIT]        -0.0892692  0.0772661  -1.155  0.24901   
Age                       -0.0187057  0.0083515  -2.240  0.02595 * 
Gendermale                 0.4637153  0.1414187   3.279  0.00118 **
Hypertension.compositeyes -0.4013863  0.1791037  -2.241  0.02587 * 
DiabetesStatusDiabetes    -0.2122018  0.1717052  -1.236  0.21763   
SmokerCurrentyes          -0.0928692  0.1350210  -0.688  0.49219   
Med.Statin.LLDyes         -0.2859631  0.1419745  -2.014  0.04502 * 
Med.all.antiplateletyes    0.2033344  0.2404618   0.846  0.39856   
GFR_MDRD                  -0.0015604  0.0038453  -0.406  0.68522   
BMI                       -0.0042388  0.0159383  -0.266  0.79049   
CAD_history                0.1856238  0.1430297   1.298  0.19551   
Stroke_history             0.0175814  0.1381435   0.127  0.89883   
Peripheral.interv         -0.1036751  0.1606087  -0.646  0.51917   
stenose50-70%             -1.3545938  1.1546015  -1.173  0.24179   
stenose70-90%             -1.0398848  1.0539217  -0.987  0.32472   
stenose90-99%             -1.1278478  1.0514119  -1.073  0.28440   
stenose100% (Occlusion)   -2.2387467  1.2187238  -1.837  0.06736 . 
hsCRP_plasma               0.0002015  0.0007230   0.279  0.78068   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.023 on 259 degrees of freedom
Multiple R-squared:  0.1172,    Adjusted R-squared:  0.05581 
F-statistic:  1.91 on 18 and 259 DF,  p-value: 0.01554

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.089269 
Standard error............: 0.077266 
Odds ratio (effect size)..: 0.915 
Lower 95% CI..............: 0.786 
Upper 95% CI..............: 1.064 
T-value...................: -1.155347 
P-value...................: 0.2490129 
R^2.......................: 0.117163 
Adjusted r^2..............: 0.055807 
Sample size of AE DB......: 2388 
Sample size of model......: 278 
Missing data %............: 88.35846 

Analysis of IL6_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + CAD_history + 
    Stroke_history + hsCRP_plasma, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]         CAD_history      Stroke_history        hsCRP_plasma  
        -0.0102133           0.1194738          -0.2522780           0.2547525           0.0004843  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.08559 -0.64622  0.01259  0.61647  2.89440 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.3292152  0.8037644   0.410  0.68225   
currentDF[, TRAIT]         0.1188011  0.0390716   3.041  0.00247 **
Age                       -0.0057304  0.0049456  -1.159  0.24705   
Gendermale                 0.0779960  0.0863697   0.903  0.36687   
Hypertension.compositeyes  0.0059062  0.1196948   0.049  0.96066   
DiabetesStatusDiabetes     0.0265491  0.0992727   0.267  0.78923   
SmokerCurrentyes           0.0556969  0.0867398   0.642  0.52105   
Med.Statin.LLDyes         -0.1269603  0.0951680  -1.334  0.18269   
Med.all.antiplateletyes    0.0842321  0.1376219   0.612  0.54074   
GFR_MDRD                  -0.0021476  0.0022014  -0.976  0.32968   
BMI                       -0.0108513  0.0105771  -1.026  0.30535   
CAD_history               -0.2196432  0.0916106  -2.398  0.01681 * 
Stroke_history             0.2495314  0.0858403   2.907  0.00379 **
Peripheral.interv         -0.0319998  0.1011919  -0.316  0.75194   
stenose50-70%              0.3367012  0.5829363   0.578  0.56376   
stenose70-90%              0.5050711  0.5620595   0.899  0.36923   
stenose90-99%              0.3553822  0.5620349   0.632  0.52743   
stenose100% (Occlusion)    0.5893787  0.7441795   0.792  0.42869   
stenose70-99%              1.0155702  0.7896134   1.286  0.19889   
hsCRP_plasma               0.0004197  0.0003397   1.235  0.21717   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9632 on 593 degrees of freedom
Multiple R-squared:  0.06585,   Adjusted R-squared:  0.03592 
F-statistic:   2.2 on 19 and 593 DF,  p-value: 0.002438

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.118801 
Standard error............: 0.039072 
Odds ratio (effect size)..: 1.126 
Lower 95% CI..............: 1.043 
Upper 95% CI..............: 1.216 
T-value...................: 3.040602 
P-value...................: 0.00246525 
R^2.......................: 0.065849 
Adjusted r^2..............: 0.035918 
Sample size of AE DB......: 2388 
Sample size of model......: 613 
Missing data %............: 74.32998 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + CAD_history + Stroke_history, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]                 Age   Med.Statin.LLDyes         CAD_history      Stroke_history  
          0.631954           -0.141333           -0.008044           -0.148043           -0.179849            0.282466  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.89600 -0.62467  0.04063  0.64149  2.69206 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.3981963  0.8034180   0.496  0.62034   
currentDF[, TRAIT]        -0.1324395  0.0412655  -3.209  0.00140 **
Age                       -0.0087393  0.0049756  -1.756  0.07953 . 
Gendermale                 0.0608222  0.0871623   0.698  0.48557   
Hypertension.compositeyes  0.0015723  0.1196609   0.013  0.98952   
DiabetesStatusDiabetes     0.0401908  0.0993075   0.405  0.68584   
SmokerCurrentyes           0.0403300  0.0867586   0.465  0.64221   
Med.Statin.LLDyes         -0.1444999  0.0955452  -1.512  0.13097   
Med.all.antiplateletyes    0.0672193  0.1376200   0.488  0.62542   
GFR_MDRD                  -0.0014576  0.0022057  -0.661  0.50898   
BMI                       -0.0077850  0.0106146  -0.733  0.46360   
CAD_history               -0.1880533  0.0919104  -2.046  0.04119 * 
Stroke_history             0.2641847  0.0858106   3.079  0.00218 **
Peripheral.interv         -0.0499322  0.1018471  -0.490  0.62413   
stenose50-70%              0.3351941  0.5826500   0.575  0.56531   
stenose70-90%              0.5366193  0.5620986   0.955  0.34013   
stenose90-99%              0.4179846  0.5624100   0.743  0.45765   
stenose100% (Occlusion)    0.6346060  0.7444406   0.852  0.39430   
stenose70-99%              1.0566801  0.7893180   1.339  0.18117   
hsCRP_plasma               0.0003459  0.0003393   1.020  0.30833   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9626 on 591 degrees of freedom
Multiple R-squared:  0.06775,   Adjusted R-squared:  0.03778 
F-statistic: 2.261 on 19 and 591 DF,  p-value: 0.001744

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.13244 
Standard error............: 0.041265 
Odds ratio (effect size)..: 0.876 
Lower 95% CI..............: 0.808 
Upper 95% CI..............: 0.95 
T-value...................: -3.209452 
P-value...................: 0.001401953 
R^2.......................: 0.06775 
Adjusted r^2..............: 0.037779 
Sample size of AE DB......: 2388 
Sample size of model......: 611 
Missing data %............: 74.41374 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD + 
    CAD_history + Stroke_history, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]   Med.Statin.LLDyes         CAD_history      Stroke_history  
           0.06268            -0.05999            -0.15096            -0.22254             0.29840  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.02020 -0.58612  0.03226  0.62982  2.78365 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.4682123  0.8229269   0.569  0.56961   
currentDF[, TRAIT]        -0.0586902  0.0423583  -1.386  0.16644   
Age                       -0.0059465  0.0051230  -1.161  0.24624   
Gendermale                 0.0795257  0.0891399   0.892  0.37270   
Hypertension.compositeyes -0.0106395  0.1234224  -0.086  0.93134   
DiabetesStatusDiabetes     0.0148287  0.1048173   0.141  0.88755   
SmokerCurrentyes           0.0750944  0.0906607   0.828  0.40786   
Med.Statin.LLDyes         -0.1554293  0.0982137  -1.583  0.11409   
Med.all.antiplateletyes    0.0491141  0.1460104   0.336  0.73672   
GFR_MDRD                  -0.0025222  0.0023388  -1.078  0.28130   
BMI                       -0.0087790  0.0109157  -0.804  0.42159   
CAD_history               -0.2018037  0.0954170  -2.115  0.03488 * 
Stroke_history             0.2799083  0.0898376   3.116  0.00193 **
Peripheral.interv         -0.0524032  0.1069737  -0.490  0.62442   
stenose50-70%              0.1592612  0.5933329   0.268  0.78848   
stenose70-90%              0.3658739  0.5687231   0.643  0.52028   
stenose90-99%              0.2478824  0.5680324   0.436  0.66273   
stenose100% (Occlusion)    0.4433709  0.7506941   0.591  0.55502   
stenose70-99%              1.9707718  1.1255726   1.751  0.08051 . 
hsCRP_plasma               0.0003700  0.0005774   0.641  0.52192   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9695 on 555 degrees of freedom
Multiple R-squared:  0.06092,   Adjusted R-squared:  0.02877 
F-statistic: 1.895 on 19 and 555 DF,  p-value: 0.01246

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.05869 
Standard error............: 0.042358 
Odds ratio (effect size)..: 0.943 
Lower 95% CI..............: 0.868 
Upper 95% CI..............: 1.025 
T-value...................: -1.385565 
P-value...................: 0.1664362 
R^2.......................: 0.060915 
Adjusted r^2..............: 0.028766 
Sample size of AE DB......: 2388 
Sample size of model......: 575 
Missing data %............: 75.92127 

Analysis of IL6R_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    DiabetesStatus + Med.Statin.LLD + GFR_MDRD + Stroke_history + 
    Peripheral.interv + stenose, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age   DiabetesStatusDiabetes        Med.Statin.LLDyes  
               0.435203                 0.178335                -0.010371                -0.167664                -0.438824  
               GFR_MDRD           Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%  
              -0.003929                 0.177631                 0.251044                 0.754456                 0.775869  
          stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
               1.023291                 0.513541                 0.658657                -0.199445  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2897 -0.6551 -0.0386  0.6427  3.1361 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.5614134  0.9022961   0.622   0.5340    
currentDF[, TRAIT]         0.1796870  0.0394568   4.554 6.38e-06 ***
Age                       -0.0092173  0.0050214  -1.836   0.0669 .  
Gendermale                 0.0124573  0.0872508   0.143   0.8865    
Hypertension.compositeyes -0.0178822  0.1207259  -0.148   0.8823    
DiabetesStatusDiabetes    -0.1532610  0.1003493  -1.527   0.1272    
SmokerCurrentyes           0.0806171  0.0878219   0.918   0.3590    
Med.Statin.LLDyes         -0.4205752  0.0971165  -4.331 1.74e-05 ***
Med.all.antiplateletyes   -0.0097451  0.1398121  -0.070   0.9445    
GFR_MDRD                  -0.0044086  0.0022316  -1.976   0.0487 *  
BMI                       -0.0088807  0.0110444  -0.804   0.4217    
CAD_history               -0.0871286  0.0931680  -0.935   0.3501    
Stroke_history             0.1557898  0.0868635   1.794   0.0734 .  
Peripheral.interv          0.2597093  0.1026991   2.529   0.0117 *  
stenose50-70%              0.8330438  0.7172129   1.162   0.2459    
stenose70-90%              0.8416904  0.6996858   1.203   0.2295    
stenose90-99%              1.0877944  0.6999532   1.554   0.1207    
stenose100% (Occlusion)    0.5369462  0.8565711   0.627   0.5310    
stenose50-99%              0.6525262  0.9835743   0.663   0.5073    
stenose70-99%             -0.1579257  0.8980699  -0.176   0.8605    
hsCRP_plasma              -0.0002230  0.0003455  -0.646   0.5188    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9796 on 597 degrees of freedom
Multiple R-squared:  0.1234,    Adjusted R-squared:  0.09405 
F-statistic: 4.203 on 20 and 597 DF,  p-value: 4.233e-09

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.179687 
Standard error............: 0.039457 
Odds ratio (effect size)..: 1.197 
Lower 95% CI..............: 1.108 
Upper 95% CI..............: 1.293 
T-value...................: 4.554018 
P-value...................: 6.382359e-06 
R^2.......................: 0.123416 
Adjusted r^2..............: 0.09405 
Sample size of AE DB......: 2388 
Sample size of model......: 618 
Missing data %............: 74.1206 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Age + DiabetesStatus + Med.Statin.LLD + 
    GFR_MDRD + Stroke_history + Peripheral.interv + stenose, 
    data = currentDF)

Coefficients:
            (Intercept)                      Age   DiabetesStatusDiabetes        Med.Statin.LLDyes                 GFR_MDRD  
               0.393940                -0.011146                -0.162148                -0.442817                -0.003293  
         Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  
               0.190041                 0.229073                 0.763493                 0.810713                 1.071875  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
               0.495589                 0.607365                -0.133855  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4555 -0.6554 -0.0491  0.6560  3.2215 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.5232714  0.9158052   0.571   0.5680    
currentDF[, TRAIT]         0.0628962  0.0426381   1.475   0.1407    
Age                       -0.0097021  0.0051290  -1.892   0.0590 .  
Gendermale                 0.0723512  0.0895023   0.808   0.4192    
Hypertension.compositeyes -0.0058188  0.1225313  -0.047   0.9621    
DiabetesStatusDiabetes    -0.1604094  0.1019520  -1.573   0.1162    
SmokerCurrentyes           0.0572906  0.0892080   0.642   0.5210    
Med.Statin.LLDyes         -0.4196048  0.0990136  -4.238 2.62e-05 ***
Med.all.antiplateletyes   -0.0179751  0.1419354  -0.127   0.8993    
GFR_MDRD                  -0.0039746  0.0022695  -1.751   0.0804 .  
BMI                       -0.0080950  0.0112566  -0.719   0.4723    
CAD_history               -0.0893468  0.0948629  -0.942   0.3467    
Stroke_history             0.1755604  0.0882045   1.990   0.0470 *  
Peripheral.interv          0.2385749  0.1049558   2.273   0.0234 *  
stenose50-70%              0.7835914  0.7282810   1.076   0.2824    
stenose70-90%              0.8101935  0.7107123   1.140   0.2548    
stenose90-99%              1.0612696  0.7112906   1.492   0.1362    
stenose100% (Occlusion)    0.4137413  0.8706729   0.475   0.6348    
stenose50-99%              0.5881169  0.9981839   0.589   0.5560    
stenose70-99%             -0.1414861  0.9116295  -0.155   0.8767    
hsCRP_plasma              -0.0002785  0.0003504  -0.795   0.4270    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.994 on 595 degrees of freedom
Multiple R-squared:  0.09475,   Adjusted R-squared:  0.06433 
F-statistic: 3.114 on 20 and 595 DF,  p-value: 6.831e-06

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: 0.062896 
Standard error............: 0.042638 
Odds ratio (effect size)..: 1.065 
Lower 95% CI..............: 0.98 
Upper 95% CI..............: 1.158 
T-value...................: 1.475115 
P-value...................: 0.1407105 
R^2.......................: 0.094754 
Adjusted r^2..............: 0.064325 
Sample size of AE DB......: 2388 
Sample size of model......: 616 
Missing data %............: 74.20436 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    DiabetesStatus + Med.Statin.LLD + GFR_MDRD + Stroke_history + 
    Peripheral.interv + stenose, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age   DiabetesStatusDiabetes        Med.Statin.LLDyes  
               0.455034                 0.091728                -0.012686                -0.203464                -0.448500  
               GFR_MDRD           Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%  
              -0.003803                 0.205875                 0.195164                 0.913705                 0.922682  
          stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
               1.166042                 0.558003                 0.607138                -0.219689  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.2856 -0.6402 -0.0580  0.6591  3.1786 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                5.857e-01  9.328e-01   0.628   0.5303    
currentDF[, TRAIT]         9.128e-02  4.377e-02   2.085   0.0375 *  
Age                       -1.185e-02  5.286e-03  -2.241   0.0254 *  
Gendermale                 3.067e-02  9.141e-02   0.336   0.7374    
Hypertension.compositeyes -8.534e-03  1.266e-01  -0.067   0.9463    
DiabetesStatusDiabetes    -1.918e-01  1.072e-01  -1.790   0.0739 .  
SmokerCurrentyes           4.729e-02  9.314e-02   0.508   0.6119    
Med.Statin.LLDyes         -4.212e-01  1.017e-01  -4.142 3.97e-05 ***
Med.all.antiplateletyes   -6.688e-02  1.502e-01  -0.445   0.6563    
GFR_MDRD                  -4.269e-03  2.405e-03  -1.775   0.0764 .  
BMI                       -5.803e-03  1.167e-02  -0.497   0.6192    
CAD_history               -1.165e-01  9.825e-02  -1.186   0.2362    
Stroke_history             1.864e-01  9.218e-02   2.022   0.0437 *  
Peripheral.interv          2.121e-01  1.098e-01   1.932   0.0539 .  
stenose50-70%              9.716e-01  7.367e-01   1.319   0.1878    
stenose70-90%              9.611e-01  7.151e-01   1.344   0.1795    
stenose90-99%              1.208e+00  7.149e-01   1.690   0.0915 .  
stenose100% (Occlusion)    5.454e-01  8.744e-01   0.624   0.5331    
stenose50-99%              6.006e-01  1.002e+00   0.599   0.5493    
stenose70-99%             -1.766e-01  1.232e+00  -0.143   0.8860    
hsCRP_plasma              -3.377e-06  5.948e-04  -0.006   0.9955    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.998 on 557 degrees of freedom
Multiple R-squared:  0.09857,   Adjusted R-squared:  0.0662 
F-statistic: 3.045 on 20 and 557 DF,  p-value: 1.121e-05

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: 0.091278 
Standard error............: 0.043771 
Odds ratio (effect size)..: 1.096 
Lower 95% CI..............: 1.006 
Upper 95% CI..............: 1.194 
T-value...................: 2.085347 
P-value...................: 0.03749243 
R^2.......................: 0.098566 
Adjusted r^2..............: 0.066198 
Sample size of AE DB......: 2388 
Sample size of model......: 578 
Missing data %............: 75.79564 

Analysis of MCP1_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD + 
    Stroke_history + stenose, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]        Med.Statin.LLDyes           Stroke_history            stenose50-70%  
               -0.42576                 -0.06444                 -0.22141                  0.22349                  0.75123  
          stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
                0.70819                  0.49049                 -0.41548                  0.49364                  1.08929  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.10369 -0.69224 -0.04003  0.63048  3.03947 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)  
(Intercept)                0.5630821  0.8283966   0.680   0.4969  
currentDF[, TRAIT]        -0.0677480  0.0395711  -1.712   0.0874 .
Age                       -0.0059183  0.0050491  -1.172   0.2416  
Gendermale                 0.1226563  0.0875344   1.401   0.1616  
Hypertension.compositeyes -0.1546531  0.1216133  -1.272   0.2040  
DiabetesStatusDiabetes    -0.1084350  0.1004437  -1.080   0.2808  
SmokerCurrentyes          -0.1235931  0.0878488  -1.407   0.1600  
Med.Statin.LLDyes         -0.2000183  0.0978361  -2.044   0.0413 *
Med.all.antiplateletyes   -0.0576239  0.1401295  -0.411   0.6811  
GFR_MDRD                  -0.0022978  0.0022513  -1.021   0.3078  
BMI                       -0.0091489  0.0107512  -0.851   0.3951  
CAD_history               -0.0672097  0.0927196  -0.725   0.4688  
Stroke_history             0.2190806  0.0876801   2.499   0.0127 *
Peripheral.interv          0.0468415  0.1027657   0.456   0.6487  
stenose50-70%              0.7003120  0.6042077   1.159   0.2469  
stenose70-90%              0.6869730  0.5825308   1.179   0.2387  
stenose90-99%              0.4732416  0.5825709   0.812   0.4169  
stenose100% (Occlusion)   -0.5522528  0.7712670  -0.716   0.4742  
stenose50-99%              0.6623472  0.9166165   0.723   0.4702  
stenose70-99%              1.1552669  0.8186149   1.411   0.1587  
hsCRP_plasma               0.0004701  0.0003520   1.336   0.1822  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9987 on 616 degrees of freedom
Multiple R-squared:  0.06066,   Adjusted R-squared:  0.03016 
F-statistic: 1.989 on 20 and 616 DF,  p-value: 0.006473

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: -0.067748 
Standard error............: 0.039571 
Odds ratio (effect size)..: 0.934 
Lower 95% CI..............: 0.865 
Upper 95% CI..............: 1.01 
T-value...................: -1.712058 
P-value...................: 0.08738937 
R^2.......................: 0.060655 
Adjusted r^2..............: 0.030157 
Sample size of AE DB......: 2388 
Sample size of model......: 637 
Missing data %............: 73.32496 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD + 
    Stroke_history + stenose, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]        Med.Statin.LLDyes           Stroke_history            stenose50-70%  
                -0.5003                  -0.1230                  -0.2297                   0.2087                   0.8328  
          stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
                 0.7985                   0.5948                  -0.2635                   0.5650                   1.1288  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.88484 -0.70661 -0.03428  0.63432  3.07566 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.5742271  0.8260859   0.695  0.48724   
currentDF[, TRAIT]        -0.1189861  0.0415453  -2.864  0.00433 **
Age                       -0.0070845  0.0050597  -1.400  0.16197   
Gendermale                 0.0721309  0.0882979   0.817  0.41430   
Hypertension.compositeyes -0.1591920  0.1212731  -1.313  0.18978   
DiabetesStatusDiabetes    -0.0980149  0.1002494  -0.978  0.32860   
SmokerCurrentyes          -0.1196738  0.0876529  -1.365  0.17265   
Med.Statin.LLDyes         -0.2126537  0.0979115  -2.172  0.03025 * 
Med.all.antiplateletyes   -0.0655511  0.1398324  -0.469  0.63939   
GFR_MDRD                  -0.0021616  0.0022506  -0.960  0.33720   
BMI                       -0.0079822  0.0107586  -0.742  0.45841   
CAD_history               -0.0566264  0.0926877  -0.611  0.54147   
Stroke_history             0.2087343  0.0874892   2.386  0.01734 * 
Peripheral.interv          0.0554092  0.1031622   0.537  0.59139   
stenose50-70%              0.7817065  0.6025178   1.297  0.19498   
stenose70-90%              0.7713051  0.5811929   1.327  0.18497   
stenose90-99%              0.5692721  0.5816193   0.979  0.32808   
stenose100% (Occlusion)   -0.3952595  0.7697019  -0.514  0.60777   
stenose50-99%              0.7193582  0.9137138   0.787  0.43142   
stenose70-99%              1.2093888  0.8164214   1.481  0.13903   
hsCRP_plasma               0.0004756  0.0003507   1.356  0.17556   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9958 on 614 degrees of freedom
Multiple R-squared:  0.06904,   Adjusted R-squared:  0.03871 
F-statistic: 2.277 on 20 and 614 DF,  p-value: 0.001266

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.118986 
Standard error............: 0.041545 
Odds ratio (effect size)..: 0.888 
Lower 95% CI..............: 0.818 
Upper 95% CI..............: 0.963 
T-value...................: -2.864007 
P-value...................: 0.004326106 
R^2.......................: 0.069035 
Adjusted r^2..............: 0.038711 
Sample size of AE DB......: 2388 
Sample size of model......: 635 
Missing data %............: 73.40871 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD + 
    Stroke_history, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]   Med.Statin.LLDyes      Stroke_history  
            0.1628             -0.1192             -0.2413              0.2636  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    hsCRP_plasma, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.96854 -0.64904 -0.03876  0.60683  3.06536 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)   
(Intercept)                0.6933010  0.8499220   0.816  0.41500   
currentDF[, TRAIT]        -0.0966390  0.0431813  -2.238  0.02560 * 
Age                       -0.0061171  0.0052516  -1.165  0.24458   
Gendermale                 0.1368620  0.0907245   1.509  0.13196   
Hypertension.compositeyes -0.1448775  0.1262510  -1.148  0.25164   
DiabetesStatusDiabetes    -0.1093450  0.1061710  -1.030  0.30349   
SmokerCurrentyes          -0.1075266  0.0920646  -1.168  0.24331   
Med.Statin.LLDyes         -0.2274730  0.1013434  -2.245  0.02517 * 
Med.all.antiplateletyes   -0.0498487  0.1486438  -0.335  0.73748   
GFR_MDRD                  -0.0029751  0.0023967  -1.241  0.21499   
BMI                       -0.0085743  0.0112184  -0.764  0.44500   
CAD_history               -0.0655035  0.0966036  -0.678  0.49800   
Stroke_history             0.2537293  0.0920522   2.756  0.00603 **
Peripheral.interv          0.0148010  0.1086944   0.136  0.89173   
stenose50-70%              0.5323579  0.6160904   0.864  0.38790   
stenose70-90%              0.5842951  0.5905842   0.989  0.32291   
stenose90-99%              0.3761791  0.5899430   0.638  0.52395   
stenose100% (Occlusion)   -0.6197206  0.7795402  -0.795  0.42695   
stenose50-99%              0.7112581  0.9245184   0.769  0.44201   
stenose70-99%              1.1103070  1.1692152   0.950  0.34270   
hsCRP_plasma              -0.0001038  0.0005997  -0.173  0.86270   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.007 on 576 degrees of freedom
Multiple R-squared:  0.06809,   Adjusted R-squared:  0.03573 
F-statistic: 2.104 on 20 and 576 DF,  p-value: 0.003462

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.096639 
Standard error............: 0.043181 
Odds ratio (effect size)..: 0.908 
Lower 95% CI..............: 0.834 
Upper 95% CI..............: 0.988 
T-value...................: -2.237981 
P-value...................: 0.02560367 
R^2.......................: 0.068089 
Adjusted r^2..............: 0.035731 
Sample size of AE DB......: 2388 
Sample size of model......: 597 
Missing data %............: 75 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    Med.all.antiplatelet + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes    Med.all.antiplateletyes          Peripheral.interv  
                  -1.1596                     0.6403                     1.0824                    -0.5324  

Degrees of Freedom: 268 Total (i.e. Null);  265 Residual
Null Deviance:      367.8 
Residual Deviance: 357.1    AIC: 365.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.721  -1.207   0.777   1.024   1.987  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -1.657e+01  8.827e+02  -0.019   0.9850  
currentDF[, PROTEIN]       1.193e-01  1.379e-01   0.865   0.3868  
Age                        6.003e-03  1.744e-02   0.344   0.7307  
Gendermale                 2.131e-02  2.932e-01   0.073   0.9421  
Hypertension.compositeyes  7.854e-01  3.754e-01   2.092   0.0364 *
DiabetesStatusDiabetes    -4.366e-01  3.729e-01  -1.171   0.2417  
SmokerCurrentyes          -9.264e-02  2.784e-01  -0.333   0.7393  
Med.Statin.LLDyes          7.990e-02  2.926e-01   0.273   0.7848  
Med.all.antiplateletyes    1.452e+00  5.763e-01   2.519   0.0118 *
GFR_MDRD                  -4.875e-03  8.062e-03  -0.605   0.5454  
BMI                       -3.826e-03  3.503e-02  -0.109   0.9130  
CAD_history                1.639e-01  2.999e-01   0.547   0.5847  
Stroke_history            -4.972e-02  2.916e-01  -0.171   0.8646  
Peripheral.interv         -5.325e-01  3.199e-01  -1.665   0.0959 .
stenose50-70%              1.555e+01  8.827e+02   0.018   0.9859  
stenose70-90%              1.533e+01  8.827e+02   0.017   0.9861  
stenose90-99%              1.478e+01  8.827e+02   0.017   0.9866  
stenose100% (Occlusion)    1.670e+01  8.827e+02   0.019   0.9849  
hsCRP_plasma               2.293e-04  1.500e-03   0.153   0.8785  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 367.81  on 268  degrees of freedom
Residual deviance: 347.27  on 250  degrees of freedom
AIC: 385.27

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.119317 
Standard error............: 0.137871 
Odds ratio (effect size)..: 1.127 
Lower 95% CI..............: 0.86 
Upper 95% CI..............: 1.476 
Z-value...................: 0.865425 
P-value...................: 0.3868057 
Hosmer and Lemeshow r^2...: 0.055845 
Cox and Snell r^2.........: 0.073515 
Nagelkerke's pseudo r^2...: 0.09865 
Sample size of AE DB......: 2388 
Sample size of model......: 269 
Missing data %............: 88.73534 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ 1, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)  
      1.265  

Degrees of Freedom: 267 Total (i.e. Null);  267 Residual
Null Deviance:      282.5 
Residual Deviance: 282.5    AIC: 284.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.03216   0.00043   0.63109   0.74468   1.15631  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)
(Intercept)                1.732e+01  2.400e+03   0.007    0.994
currentDF[, PROTEIN]      -1.979e-01  1.619e-01  -1.223    0.221
Age                        1.704e-03  2.059e-02   0.083    0.934
Gendermale                -2.853e-01  3.592e-01  -0.794    0.427
Hypertension.compositeyes  1.133e-01  4.443e-01   0.255    0.799
DiabetesStatusDiabetes     9.509e-02  4.506e-01   0.211    0.833
SmokerCurrentyes           3.723e-01  3.388e-01   1.099    0.272
Med.Statin.LLDyes          5.238e-03  3.450e-01   0.015    0.988
Med.all.antiplateletyes    9.172e-01  5.669e-01   1.618    0.106
GFR_MDRD                  -2.450e-03  9.592e-03  -0.255    0.798
BMI                       -3.337e-02  4.034e-02  -0.827    0.408
CAD_history                1.347e-01  3.525e-01   0.382    0.702
Stroke_history             2.922e-01  3.577e-01   0.817    0.414
Peripheral.interv         -4.212e-01  3.606e-01  -1.168    0.243
stenose50-70%             -9.056e-01  2.618e+03   0.000    1.000
stenose70-90%             -1.587e+01  2.400e+03  -0.007    0.995
stenose90-99%             -1.618e+01  2.400e+03  -0.007    0.995
stenose100% (Occlusion)   -3.871e-01  2.662e+03   0.000    1.000
hsCRP_plasma               2.233e-05  1.768e-03   0.013    0.990

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 282.52  on 267  degrees of freedom
Residual deviance: 268.34  on 249  degrees of freedom
AIC: 306.34

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.197947 
Standard error............: 0.161913 
Odds ratio (effect size)..: 0.82 
Lower 95% CI..............: 0.597 
Upper 95% CI..............: 1.127 
Z-value...................: -1.222556 
P-value...................: 0.2214974 
Hosmer and Lemeshow r^2...: 0.050195 
Cox and Snell r^2.........: 0.05154 
Nagelkerke's pseudo r^2...: 0.079106 
Sample size of AE DB......: 2388 
Sample size of model......: 268 
Missing data %............: 88.77722 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite + 
    DiabetesStatus + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)                 Gendermale  Hypertension.compositeyes     DiabetesStatusDiabetes                        BMI  
                 -1.50676                    0.92764                    0.91937                   -0.96551                    0.06233  

Degrees of Freedom: 268 Total (i.e. Null);  264 Residual
Null Deviance:      277.9 
Residual Deviance: 258.8    AIC: 268.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3536   0.3810   0.5203   0.6864   1.4348  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.139e+01  8.827e+02   0.013  0.98971   
currentDF[, PROTEIN]       1.642e-01  1.690e-01   0.972  0.33115   
Age                       -4.375e-04  2.173e-02  -0.020  0.98394   
Gendermale                 1.080e+00  3.462e-01   3.119  0.00181 **
Hypertension.compositeyes  8.208e-01  4.205e-01   1.952  0.05093 . 
DiabetesStatusDiabetes    -8.081e-01  4.286e-01  -1.886  0.05935 . 
SmokerCurrentyes           3.842e-01  3.576e-01   1.074  0.28266   
Med.Statin.LLDyes         -3.148e-02  3.784e-01  -0.083  0.93371   
Med.all.antiplateletyes    6.024e-01  6.006e-01   1.003  0.31587   
GFR_MDRD                  -1.040e-02  1.041e-02  -0.999  0.31763   
BMI                        4.420e-02  4.424e-02   0.999  0.31774   
CAD_history               -4.480e-02  3.804e-01  -0.118  0.90624   
Stroke_history            -6.954e-03  3.719e-01  -0.019  0.98508   
Peripheral.interv         -1.508e-01  3.913e-01  -0.385  0.69991   
stenose50-70%             -1.453e+01  8.827e+02  -0.016  0.98686   
stenose70-90%             -1.212e+01  8.827e+02  -0.014  0.98905   
stenose90-99%             -1.233e+01  8.827e+02  -0.014  0.98886   
stenose100% (Occlusion)   -1.233e+01  8.827e+02  -0.014  0.98885   
hsCRP_plasma              -6.836e-04  1.841e-03  -0.371  0.71035   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 277.85  on 268  degrees of freedom
Residual deviance: 248.92  on 250  degrees of freedom
AIC: 286.92

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.164233 
Standard error............: 0.168998 
Odds ratio (effect size)..: 1.178 
Lower 95% CI..............: 0.846 
Upper 95% CI..............: 1.641 
Z-value...................: 0.971804 
P-value...................: 0.3311482 
Hosmer and Lemeshow r^2...: 0.104119 
Cox and Snell r^2.........: 0.101965 
Nagelkerke's pseudo r^2...: 0.158323 
Sample size of AE DB......: 2388 
Sample size of model......: 269 
Missing data %............: 88.73534 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)          Age   Gendermale  
   -1.16709      0.02764      0.68346  

Degrees of Freedom: 268 Total (i.e. Null);  266 Residual
Null Deviance:      299.8 
Residual Deviance: 290.9    AIC: 296.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.17959   0.04718   0.63393   0.75588   1.32398  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                10.824949 882.746852   0.012   0.9902  
currentDF[, PROTEIN]       -0.061940   0.155846  -0.397   0.6910  
Age                         0.029119   0.019769   1.473   0.1408  
Gendermale                  0.759603   0.320114   2.373   0.0176 *
Hypertension.compositeyes   0.137913   0.416919   0.331   0.7408  
DiabetesStatusDiabetes     -0.485078   0.403890  -1.201   0.2297  
SmokerCurrentyes            0.390151   0.322786   1.209   0.2268  
Med.Statin.LLDyes          -0.103067   0.340278  -0.303   0.7620  
Med.all.antiplateletyes     0.293274   0.587081   0.500   0.6174  
GFR_MDRD                   -0.001683   0.009405  -0.179   0.8580  
BMI                         0.023201   0.038976   0.595   0.5517  
CAD_history                 0.098145   0.346738   0.283   0.7771  
Stroke_history             -0.119419   0.339051  -0.352   0.7247  
Peripheral.interv           0.185422   0.374163   0.496   0.6202  
stenose50-70%             -13.360543 882.744229  -0.015   0.9879  
stenose70-90%             -13.113999 882.743629  -0.015   0.9881  
stenose90-99%             -13.082823 882.743610  -0.015   0.9882  
stenose100% (Occlusion)   -12.806824 882.744499  -0.015   0.9884  
hsCRP_plasma                0.003722   0.004579   0.813   0.4163  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 299.76  on 268  degrees of freedom
Residual deviance: 285.14  on 250  degrees of freedom
AIC: 323.14

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: IPH 
Effect size...............: -0.06194 
Standard error............: 0.155846 
Odds ratio (effect size)..: 0.94 
Lower 95% CI..............: 0.693 
Upper 95% CI..............: 1.276 
Z-value...................: -0.397442 
P-value...................: 0.6910419 
Hosmer and Lemeshow r^2...: 0.048752 
Cox and Snell r^2.........: 0.052877 
Nagelkerke's pseudo r^2...: 0.078701 
Sample size of AE DB......: 2388 
Sample size of model......: 269 
Missing data %............: 88.73534 

Analysis of MCP1_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    DiabetesStatus + Med.all.antiplatelet + Peripheral.interv, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes     DiabetesStatusDiabetes    Med.all.antiplateletyes          Peripheral.interv  
                  -0.6342                     0.5475                    -0.6292                     0.7942                    -0.6265  

Degrees of Freedom: 282 Total (i.e. Null);  278 Residual
Null Deviance:      385.1 
Residual Deviance: 372.2    AIC: 382.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.8687  -1.1890   0.7609   0.9977   2.0563  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -1.603e+01  8.827e+02  -0.018   0.9855  
currentDF[, PROTEIN]      -1.311e-01  1.265e-01  -1.036   0.3002  
Age                        1.166e-02  1.704e-02   0.684   0.4941  
Gendermale                 1.499e-01  2.926e-01   0.512   0.6083  
Hypertension.compositeyes  5.881e-01  3.663e-01   1.606   0.1084  
DiabetesStatusDiabetes    -7.689e-01  3.511e-01  -2.190   0.0285 *
SmokerCurrentyes           1.482e-01  2.779e-01   0.533   0.5939  
Med.Statin.LLDyes         -1.047e-01  2.928e-01  -0.358   0.7207  
Med.all.antiplateletyes    1.198e+00  5.103e-01   2.348   0.0189 *
GFR_MDRD                  -3.765e-03  7.829e-03  -0.481   0.6306  
BMI                        6.503e-03  3.266e-02   0.199   0.8422  
CAD_history                1.458e-01  2.957e-01   0.493   0.6219  
Stroke_history            -3.861e-01  2.831e-01  -1.364   0.1726  
Peripheral.interv         -6.597e-01  3.226e-01  -2.045   0.0409 *
stenose50-70%              1.484e+01  8.827e+02   0.017   0.9866  
stenose70-90%              1.476e+01  8.827e+02   0.017   0.9867  
stenose90-99%              1.408e+01  8.827e+02   0.016   0.9873  
stenose100% (Occlusion)    1.553e+01  8.827e+02   0.018   0.9860  
hsCRP_plasma               1.219e-04  1.575e-03   0.077   0.9383  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 385.14  on 282  degrees of freedom
Residual deviance: 360.92  on 264  degrees of freedom
AIC: 398.92

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.13109 
Standard error............: 0.126542 
Odds ratio (effect size)..: 0.877 
Lower 95% CI..............: 0.684 
Upper 95% CI..............: 1.124 
Z-value...................: -1.035945 
P-value...................: 0.3002277 
Hosmer and Lemeshow r^2...: 0.062882 
Cox and Snell r^2.........: 0.082016 
Nagelkerke's pseudo r^2...: 0.110301 
Sample size of AE DB......: 2388 
Sample size of model......: 283 
Missing data %............: 88.14908 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Stroke_history, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]        Stroke_history  
              1.2479               -0.4765                0.4920  

Degrees of Freedom: 281 Total (i.e. Null);  279 Residual
Null Deviance:      297.1 
Residual Deviance: 283.9    AIC: 289.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.34755   0.06012   0.58686   0.73422   1.31594  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.748e+01  2.400e+03   0.007  0.99419   
currentDF[, PROTEIN]      -5.045e-01  1.592e-01  -3.170  0.00153 **
Age                       -5.547e-03  2.045e-02  -0.271  0.78618   
Gendermale                -7.921e-02  3.634e-01  -0.218  0.82743   
Hypertension.compositeyes  2.925e-02  4.378e-01   0.067  0.94673   
DiabetesStatusDiabetes     2.155e-01  4.403e-01   0.490  0.62446   
SmokerCurrentyes           3.666e-01  3.426e-01   1.070  0.28459   
Med.Statin.LLDyes          4.374e-02  3.430e-01   0.128  0.89854   
Med.all.antiplateletyes    1.015e+00  5.424e-01   1.871  0.06138 . 
GFR_MDRD                  -5.296e-03  9.597e-03  -0.552  0.58102   
BMI                       -2.040e-02  4.045e-02  -0.504  0.61395   
CAD_history                4.851e-02  3.488e-01   0.139  0.88938   
Stroke_history             3.564e-01  3.577e-01   0.996  0.31907   
Peripheral.interv         -4.691e-01  3.713e-01  -1.264  0.20639   
stenose50-70%             -7.343e-01  2.623e+03   0.000  0.99978   
stenose70-90%             -1.569e+01  2.400e+03  -0.007  0.99478   
stenose90-99%             -1.608e+01  2.400e+03  -0.007  0.99465   
stenose100% (Occlusion)   -5.321e-01  2.709e+03   0.000  0.99984   
hsCRP_plasma              -6.307e-05  1.702e-03  -0.037  0.97043   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 297.07  on 281  degrees of freedom
Residual deviance: 272.39  on 263  degrees of freedom
AIC: 310.39

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.50448 
Standard error............: 0.159154 
Odds ratio (effect size)..: 0.604 
Lower 95% CI..............: 0.442 
Upper 95% CI..............: 0.825 
Z-value...................: -3.16975 
P-value...................: 0.001525702 
Hosmer and Lemeshow r^2...: 0.083107 
Cox and Snell r^2.........: 0.083826 
Nagelkerke's pseudo r^2...: 0.128712 
Sample size of AE DB......: 2388 
Sample size of model......: 282 
Missing data %............: 88.19096 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Hypertension.composite, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]                 Gendermale  Hypertension.compositeyes  
                   0.0789                     0.5522                     0.5932                     1.0357  

Degrees of Freedom: 282 Total (i.e. Null);  279 Residual
Null Deviance:      295 
Residual Deviance: 270.6    AIC: 278.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3141   0.3124   0.5145   0.6850   1.5427  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.105e+01  8.827e+02   0.013 0.990009    
currentDF[, PROTEIN]       5.645e-01  1.658e-01   3.405 0.000661 ***
Age                        9.354e-03  2.085e-02   0.449 0.653654    
Gendermale                 6.345e-01  3.419e-01   1.856 0.063462 .  
Hypertension.compositeyes  9.458e-01  4.203e-01   2.250 0.024443 *  
DiabetesStatusDiabetes    -2.360e-01  4.246e-01  -0.556 0.578317    
SmokerCurrentyes           4.125e-01  3.493e-01   1.181 0.237653    
Med.Statin.LLDyes          7.017e-03  3.696e-01   0.019 0.984853    
Med.all.antiplateletyes    1.850e-01  5.906e-01   0.313 0.754077    
GFR_MDRD                  -7.158e-03  9.898e-03  -0.723 0.469559    
BMI                        3.406e-02  4.044e-02   0.842 0.399695    
CAD_history                4.642e-03  3.745e-01   0.012 0.990112    
Stroke_history             6.665e-02  3.605e-01   0.185 0.853332    
Peripheral.interv         -3.889e-02  3.986e-01  -0.098 0.922261    
stenose50-70%             -1.430e+01  8.827e+02  -0.016 0.987078    
stenose70-90%             -1.185e+01  8.827e+02  -0.013 0.989291    
stenose90-99%             -1.235e+01  8.827e+02  -0.014 0.988834    
stenose100% (Occlusion)   -1.216e+01  8.827e+02  -0.014 0.989013    
hsCRP_plasma              -4.565e-04  1.916e-03  -0.238 0.811647    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 295.01  on 282  degrees of freedom
Residual deviance: 259.25  on 264  degrees of freedom
AIC: 297.25

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.564483 
Standard error............: 0.165767 
Odds ratio (effect size)..: 1.759 
Lower 95% CI..............: 1.271 
Upper 95% CI..............: 2.434 
Z-value...................: 3.405272 
P-value...................: 0.0006609828 
Hosmer and Lemeshow r^2...: 0.121223 
Cox and Snell r^2.........: 0.118709 
Nagelkerke's pseudo r^2...: 0.183361 
Sample size of AE DB......: 2388 
Sample size of model......: 283 
Missing data %............: 88.14908 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + DiabetesStatus, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
           (Intercept)              Gendermale  DiabetesStatusDiabetes  
                0.6474                  0.8393                 -0.5088  

Degrees of Freedom: 282 Total (i.e. Null);  280 Residual
Null Deviance:      316.6 
Residual Deviance: 306.3    AIC: 312.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.21255   0.03224   0.60894   0.76603   1.53045  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.172e+01  8.827e+02   0.013  0.98941   
currentDF[, PROTEIN]      -1.546e-01  1.461e-01  -1.059  0.28982   
Age                        1.509e-02  1.947e-02   0.775  0.43807   
Gendermale                 9.703e-01  3.200e-01   3.032  0.00243 **
Hypertension.compositeyes  9.932e-02  4.094e-01   0.243  0.80834   
DiabetesStatusDiabetes    -5.111e-01  3.750e-01  -1.363  0.17298   
SmokerCurrentyes           4.463e-01  3.232e-01   1.381  0.16740   
Med.Statin.LLDyes         -3.783e-01  3.469e-01  -1.091  0.27549   
Med.all.antiplateletyes    4.714e-01  5.319e-01   0.886  0.37550   
GFR_MDRD                  -8.638e-04  9.135e-03  -0.095  0.92467   
BMI                        1.923e-02  3.600e-02   0.534  0.59323   
CAD_history                2.176e-01  3.461e-01   0.629  0.52963   
Stroke_history            -2.648e-02  3.306e-01  -0.080  0.93617   
Peripheral.interv          2.434e-01  3.828e-01   0.636  0.52492   
stenose50-70%             -1.248e+01  8.827e+02  -0.014  0.98872   
stenose70-90%             -1.316e+01  8.827e+02  -0.015  0.98811   
stenose90-99%             -1.314e+01  8.827e+02  -0.015  0.98813   
stenose100% (Occlusion)   -1.352e+01  8.827e+02  -0.015  0.98778   
hsCRP_plasma               3.179e-03  4.363e-03   0.729  0.46623   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 316.62  on 282  degrees of freedom
Residual deviance: 297.37  on 264  degrees of freedom
AIC: 335.37

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: IPH 
Effect size...............: -0.154647 
Standard error............: 0.146099 
Odds ratio (effect size)..: 0.857 
Lower 95% CI..............: 0.643 
Upper 95% CI..............: 1.141 
Z-value...................: -1.058506 
P-value...................: 0.2898247 
Hosmer and Lemeshow r^2...: 0.060794 
Cox and Snell r^2.........: 0.065755 
Nagelkerke's pseudo r^2...: 0.097656 
Sample size of AE DB......: 2388 
Sample size of model......: 283 
Missing data %............: 88.14908 

Analysis of IL6_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent + 
    CAD_history + Peripheral.interv + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)                      Age         SmokerCurrentyes              CAD_history        Peripheral.interv  
               -2.03767                  0.01906                  0.31759                  0.28534                 -0.39470  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose70-99%  
               -0.26332                  0.44676                  0.69678                  1.70987                -14.15643  

Degrees of Freedom: 613 Total (i.e. Null);  604 Residual
Null Deviance:      849.9 
Residual Deviance: 824.6    AIC: 844.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.639  -1.124  -0.782   1.160   1.713  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -3.125e+00  1.750e+00  -1.786   0.0741 .
currentDF[, PROTEIN]      -2.045e-02  8.657e-02  -0.236   0.8133  
Age                        2.150e-02  1.055e-02   2.037   0.0416 *
Gendermale                 3.066e-02  1.826e-01   0.168   0.8667  
Hypertension.compositeyes  2.947e-01  2.552e-01   1.155   0.2482  
DiabetesStatusDiabetes    -2.433e-02  2.117e-01  -0.115   0.9085  
SmokerCurrentyes           3.486e-01  1.844e-01   1.891   0.0586 .
Med.Statin.LLDyes         -2.635e-01  2.026e-01  -1.301   0.1933  
Med.all.antiplateletyes    6.608e-02  2.939e-01   0.225   0.8221  
GFR_MDRD                   4.748e-03  4.713e-03   1.007   0.3137  
BMI                        1.441e-02  2.247e-02   0.642   0.5212  
CAD_history                2.976e-01  1.954e-01   1.523   0.1277  
Stroke_history             4.809e-03  1.840e-01   0.026   0.9791  
Peripheral.interv         -3.832e-01  2.158e-01  -1.776   0.0758 .
stenose50-70%             -1.930e-01  1.300e+00  -0.148   0.8820  
stenose70-90%              5.134e-01  1.251e+00   0.410   0.6815  
stenose90-99%              7.524e-01  1.251e+00   0.602   0.5474  
stenose100% (Occlusion)    1.842e+00  1.718e+00   1.072   0.2838  
stenose70-99%             -1.407e+01  4.293e+02  -0.033   0.9739  
hsCRP_plasma              -3.991e-04  7.679e-04  -0.520   0.6032  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 849.91  on 613  degrees of freedom
Residual deviance: 819.87  on 594  degrees of freedom
AIC: 859.87

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.020447 
Standard error............: 0.086566 
Odds ratio (effect size)..: 0.98 
Lower 95% CI..............: 0.827 
Upper 95% CI..............: 1.161 
Z-value...................: -0.236202 
P-value...................: 0.8132758 
Hosmer and Lemeshow r^2...: 0.035343 
Cox and Snell r^2.........: 0.047745 
Nagelkerke's pseudo r^2...: 0.063704 
Sample size of AE DB......: 2388 
Sample size of model......: 614 
Missing data %............: 74.28811 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerCurrent + CAD_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]      SmokerCurrentyes           CAD_history  
              0.9687               -0.3028                0.4678                0.5081  

Degrees of Freedom: 616 Total (i.e. Null);  613 Residual
Null Deviance:      658.4 
Residual Deviance: 639.9    AIC: 647.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3183   0.3944   0.6278   0.7480   1.1415  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.308e+01  8.347e+02   0.016  0.98750   
currentDF[, PROTEIN]      -3.246e-01  1.069e-01  -3.036  0.00239 **
Age                        6.979e-03  1.231e-02   0.567  0.57092   
Gendermale                 7.282e-02  2.168e-01   0.336  0.73693   
Hypertension.compositeyes  1.616e-01  2.881e-01   0.561  0.57480   
DiabetesStatusDiabetes     2.080e-01  2.608e-01   0.797  0.42523   
SmokerCurrentyes           5.486e-01  2.293e-01   2.393  0.01671 * 
Med.Statin.LLDyes         -1.187e-01  2.418e-01  -0.491  0.62337   
Med.all.antiplateletyes    5.316e-01  3.297e-01   1.612  0.10691   
GFR_MDRD                   2.491e-03  5.598e-03   0.445  0.65630   
BMI                        2.510e-02  2.805e-02   0.895  0.37089   
CAD_history                5.359e-01  2.479e-01   2.162  0.03064 * 
Stroke_history             3.038e-01  2.237e-01   1.358  0.17445   
Peripheral.interv          9.879e-02  2.631e-01   0.376  0.70726   
stenose50-70%             -1.414e+01  8.347e+02  -0.017  0.98648   
stenose70-90%             -1.421e+01  8.347e+02  -0.017  0.98642   
stenose90-99%             -1.415e+01  8.347e+02  -0.017  0.98647   
stenose100% (Occlusion)    6.239e-01  1.074e+03   0.001  0.99954   
stenose70-99%             -1.443e+01  8.347e+02  -0.017  0.98620   
hsCRP_plasma              -7.901e-04  7.316e-04  -1.080  0.28013   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 658.36  on 616  degrees of freedom
Residual deviance: 627.90  on 597  degrees of freedom
AIC: 667.9

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.324626 
Standard error............: 0.106915 
Odds ratio (effect size)..: 0.723 
Lower 95% CI..............: 0.586 
Upper 95% CI..............: 0.891 
Z-value...................: -3.036315 
P-value...................: 0.002394889 
Hosmer and Lemeshow r^2...: 0.046255 
Cox and Snell r^2.........: 0.048157 
Nagelkerke's pseudo r^2...: 0.073414 
Sample size of AE DB......: 2388 
Sample size of model......: 617 
Missing data %............: 74.16248 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale     Peripheral.interv  
              0.5293                0.6114                0.8125               -0.4397  

Degrees of Freedom: 616 Total (i.e. Null);  613 Residual
Null Deviance:      743.2 
Residual Deviance: 680.1    AIC: 688.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2677  -1.0403   0.6028   0.8173   2.0610  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.352e+01  5.059e+02   0.027   0.9787    
currentDF[, PROTEIN]       6.055e-01  1.037e-01   5.838 5.28e-09 ***
Age                        6.235e-03  1.200e-02   0.520   0.6033    
Gendermale                 8.432e-01  2.013e-01   4.189 2.80e-05 ***
Hypertension.compositeyes  1.228e-01  2.865e-01   0.429   0.6682    
DiabetesStatusDiabetes    -3.444e-02  2.411e-01  -0.143   0.8864    
SmokerCurrentyes          -5.286e-03  2.111e-01  -0.025   0.9800    
Med.Statin.LLDyes         -5.567e-02  2.356e-01  -0.236   0.8132    
Med.all.antiplateletyes   -5.235e-03  3.311e-01  -0.016   0.9874    
GFR_MDRD                  -1.149e-03  5.415e-03  -0.212   0.8320    
BMI                        8.579e-03  2.474e-02   0.347   0.7288    
CAD_history               -6.148e-02  2.220e-01  -0.277   0.7818    
Stroke_history             2.278e-01  2.165e-01   1.052   0.2928    
Peripheral.interv         -4.543e-01  2.339e-01  -1.942   0.0521 .  
stenose50-70%             -1.407e+01  5.059e+02  -0.028   0.9778    
stenose70-90%             -1.371e+01  5.059e+02  -0.027   0.9784    
stenose90-99%             -1.360e+01  5.059e+02  -0.027   0.9786    
stenose100% (Occlusion)   -1.511e+01  5.059e+02  -0.030   0.9762    
stenose70-99%             -1.475e+01  5.059e+02  -0.029   0.9767    
hsCRP_plasma               8.231e-04  1.316e-03   0.625   0.5318    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 743.18  on 616  degrees of freedom
Residual deviance: 671.07  on 597  degrees of freedom
AIC: 711.07

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.605506 
Standard error............: 0.103716 
Odds ratio (effect size)..: 1.832 
Lower 95% CI..............: 1.495 
Upper 95% CI..............: 2.245 
Z-value...................: 5.838117 
P-value...................: 5.279419e-09 
Hosmer and Lemeshow r^2...: 0.097027 
Cox and Snell r^2.........: 0.110299 
Nagelkerke's pseudo r^2...: 0.157534 
Sample size of AE DB......: 2388 
Sample size of model......: 617 
Missing data %............: 74.16248 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)               Gendermale         SmokerCurrentyes        Med.Statin.LLDyes  Med.all.antiplateletyes  
                -0.2863                   0.6645                   0.4139                  -0.3598                   0.4213  

Degrees of Freedom: 615 Total (i.e. Null);  611 Residual
Null Deviance:      828.8 
Residual Deviance: 806.9    AIC: 816.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0878  -1.2415   0.8000   0.9943   1.5548  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                0.3978635  1.7606654   0.226 0.821222    
currentDF[, PROTEIN]       0.1303245  0.0892699   1.460 0.144320    
Age                       -0.0008199  0.0107395  -0.076 0.939143    
Gendermale                 0.7038625  0.1860745   3.783 0.000155 ***
Hypertension.compositeyes -0.3438930  0.2655006  -1.295 0.195230    
DiabetesStatusDiabetes    -0.1476838  0.2140216  -0.690 0.490168    
SmokerCurrentyes           0.3955331  0.1923278   2.057 0.039729 *  
Med.Statin.LLDyes         -0.3590541  0.2126520  -1.688 0.091323 .  
Med.all.antiplateletyes    0.3879883  0.2955953   1.313 0.189329    
GFR_MDRD                  -0.0050049  0.0048318  -1.036 0.300281    
BMI                        0.0091710  0.0228344   0.402 0.687956    
CAD_history                0.2170901  0.2010528   1.080 0.280246    
Stroke_history             0.2138322  0.1908381   1.120 0.262505    
Peripheral.interv          0.1016178  0.2212728   0.459 0.646060    
stenose50-70%             -0.7340905  1.2832124  -0.572 0.567273    
stenose70-90%             -0.4135027  1.2420292  -0.333 0.739191    
stenose90-99%             -0.1350682  1.2420013  -0.109 0.913400    
stenose100% (Occlusion)   -0.8891041  1.6032226  -0.555 0.579187    
stenose70-99%              0.3924623  1.7022428   0.231 0.817660    
hsCRP_plasma              -0.0006085  0.0007988  -0.762 0.446223    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 828.82  on 615  degrees of freedom
Residual deviance: 794.02  on 596  degrees of freedom
AIC: 834.02

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.130324 
Standard error............: 0.08927 
Odds ratio (effect size)..: 1.139 
Lower 95% CI..............: 0.956 
Upper 95% CI..............: 1.357 
Z-value...................: 1.459892 
P-value...................: 0.1443198 
Hosmer and Lemeshow r^2...: 0.041991 
Cox and Snell r^2.........: 0.054933 
Nagelkerke's pseudo r^2...: 0.074274 
Sample size of AE DB......: 2388 
Sample size of model......: 616 
Missing data %............: 74.20436 

Analysis of IL6R_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent + 
    CAD_history + Peripheral.interv + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)                      Age         SmokerCurrentyes              CAD_history        Peripheral.interv  
               -1.39761                  0.01805                  0.26766                  0.28298                 -0.44359  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%  
               -0.76341                 -0.08234                  0.14517                  1.15737                -15.67282  
          stenose70-99%  
              -15.68045  

Degrees of Freedom: 618 Total (i.e. Null);  608 Residual
Null Deviance:      856.9 
Residual Deviance: 830.8    AIC: 852.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.5362  -1.1336  -0.7777   1.1683   1.7316  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -2.244e+00  1.864e+00  -1.203   0.2288  
currentDF[, PROTEIN]      -2.870e-02  8.420e-02  -0.341   0.7332  
Age                        2.035e-02  1.052e-02   1.933   0.0532 .
Gendermale                -7.018e-03  1.812e-01  -0.039   0.9691  
Hypertension.compositeyes  2.763e-01  2.520e-01   1.097   0.2729  
DiabetesStatusDiabetes    -1.002e-01  2.111e-01  -0.474   0.6352  
SmokerCurrentyes           2.927e-01  1.830e-01   1.600   0.1097  
Med.Statin.LLDyes         -1.664e-01  2.054e-01  -0.810   0.4177  
Med.all.antiplateletyes    1.380e-01  2.936e-01   0.470   0.6384  
GFR_MDRD                   3.991e-03  4.690e-03   0.851   0.3947  
BMI                        3.507e-03  2.295e-02   0.153   0.8786  
CAD_history                2.929e-01  1.945e-01   1.506   0.1320  
Stroke_history            -1.298e-02  1.821e-01  -0.071   0.9432  
Peripheral.interv         -4.343e-01  2.163e-01  -2.008   0.0447 *
stenose50-70%             -6.697e-01  1.476e+00  -0.454   0.6501  
stenose70-90%              2.169e-02  1.434e+00   0.015   0.9879  
stenose90-99%              2.411e-01  1.436e+00   0.168   0.8666  
stenose100% (Occlusion)    1.304e+00  1.854e+00   0.703   0.4817  
stenose50-99%             -1.567e+01  1.029e+03  -0.015   0.9879  
stenose70-99%             -1.564e+01  7.110e+02  -0.022   0.9824  
hsCRP_plasma              -4.118e-04  7.572e-04  -0.544   0.5865  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 856.94  on 618  degrees of freedom
Residual deviance: 827.42  on 598  degrees of freedom
AIC: 869.42

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.028703 
Standard error............: 0.0842 
Odds ratio (effect size)..: 0.972 
Lower 95% CI..............: 0.824 
Upper 95% CI..............: 1.146 
Z-value...................: -0.340892 
P-value...................: 0.7331848 
Hosmer and Lemeshow r^2...: 0.034446 
Cox and Snell r^2.........: 0.046567 
Nagelkerke's pseudo r^2...: 0.062129 
Sample size of AE DB......: 2388 
Sample size of model......: 619 
Missing data %............: 74.07873 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent + 
    Med.all.antiplatelet + CAD_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)         SmokerCurrentyes  Med.all.antiplateletyes              CAD_history  
                 0.4945                   0.4512                   0.4820                   0.5581  

Degrees of Freedom: 621 Total (i.e. Null);  618 Residual
Null Deviance:      665.8 
Residual Deviance: 653.9    AIC: 661.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2594   0.4425   0.6377   0.7661   1.1346  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.310e+01  1.011e+03   0.013   0.9897  
currentDF[, PROTEIN]       7.121e-03  1.002e-01   0.071   0.9433  
Age                        9.646e-03  1.226e-02   0.787   0.4312  
Gendermale                 1.459e-01  2.117e-01   0.689   0.4909  
Hypertension.compositeyes  1.036e-01  2.844e-01   0.364   0.7157  
DiabetesStatusDiabetes     2.299e-01  2.587e-01   0.889   0.3742  
SmokerCurrentyes           5.375e-01  2.260e-01   2.379   0.0174 *
Med.Statin.LLDyes         -2.465e-02  2.401e-01  -0.103   0.9182  
Med.all.antiplateletyes    5.599e-01  3.220e-01   1.739   0.0820 .
GFR_MDRD                   1.810e-03  5.550e-03   0.326   0.7443  
BMI                        2.302e-02  2.772e-02   0.830   0.4064  
CAD_history                5.432e-01  2.439e-01   2.227   0.0260 *
Stroke_history             1.954e-01  2.189e-01   0.893   0.3720  
Peripheral.interv          1.382e-01  2.615e-01   0.529   0.5971  
stenose50-70%             -1.432e+01  1.011e+03  -0.014   0.9887  
stenose70-90%             -1.446e+01  1.011e+03  -0.014   0.9886  
stenose90-99%             -1.431e+01  1.011e+03  -0.014   0.9887  
stenose100% (Occlusion)    3.060e-01  1.229e+03   0.000   0.9998  
stenose50-99%             -4.011e-01  1.442e+03   0.000   0.9998  
stenose70-99%             -1.468e+01  1.011e+03  -0.015   0.9884  
hsCRP_plasma              -8.393e-04  7.374e-04  -1.138   0.2551  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 665.84  on 621  degrees of freedom
Residual deviance: 643.58  on 601  degrees of freedom
AIC: 685.58

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.007121 
Standard error............: 0.10019 
Odds ratio (effect size)..: 1.007 
Lower 95% CI..............: 0.828 
Upper 95% CI..............: 1.226 
Z-value...................: 0.071079 
P-value...................: 0.943335 
Hosmer and Lemeshow r^2...: 0.033434 
Cox and Snell r^2.........: 0.035158 
Nagelkerke's pseudo r^2...: 0.0535 
Sample size of AE DB......: 2388 
Sample size of model......: 622 
Missing data %............: 73.9531 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale        Stroke_history     Peripheral.interv  
              0.3305                0.2398                0.8514                0.3619               -0.4835  

Degrees of Freedom: 621 Total (i.e. Null);  617 Residual
Null Deviance:      750.2 
Residual Deviance: 714.3    AIC: 724.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1084  -1.1775   0.6665   0.8395   1.7256  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.329e+01  6.236e+02   0.021   0.9830    
currentDF[, PROTEIN]       2.360e-01  9.449e-02   2.497   0.0125 *  
Age                        9.854e-03  1.165e-02   0.846   0.3975    
Gendermale                 8.899e-01  1.953e-01   4.557  5.2e-06 ***
Hypertension.compositeyes  1.338e-01  2.763e-01   0.484   0.6281    
DiabetesStatusDiabetes    -1.547e-02  2.337e-01  -0.066   0.9472    
SmokerCurrentyes           5.933e-02  2.054e-01   0.289   0.7727    
Med.Statin.LLDyes          6.082e-02  2.329e-01   0.261   0.7940    
Med.all.antiplateletyes    5.539e-02  3.245e-01   0.171   0.8645    
GFR_MDRD                   1.544e-04  5.301e-03   0.029   0.9768    
BMI                       -4.085e-03  2.512e-02  -0.163   0.8708    
CAD_history               -1.715e-01  2.159e-01  -0.794   0.4271    
Stroke_history             3.399e-01  2.103e-01   1.616   0.1061    
Peripheral.interv         -4.900e-01  2.288e-01  -2.142   0.0322 *  
stenose50-70%             -1.406e+01  6.236e+02  -0.023   0.9820    
stenose70-90%             -1.370e+01  6.236e+02  -0.022   0.9825    
stenose90-99%             -1.372e+01  6.236e+02  -0.022   0.9824    
stenose100% (Occlusion)   -1.499e+01  6.236e+02  -0.024   0.9808    
stenose50-99%             -2.925e+01  8.788e+02  -0.033   0.9734    
stenose70-99%             -1.440e+01  6.236e+02  -0.023   0.9816    
hsCRP_plasma               1.055e-03  1.180e-03   0.894   0.3712    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 750.18  on 621  degrees of freedom
Residual deviance: 702.66  on 601  degrees of freedom
AIC: 744.66

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.235974 
Standard error............: 0.09449 
Odds ratio (effect size)..: 1.266 
Lower 95% CI..............: 1.052 
Upper 95% CI..............: 1.524 
Z-value...................: 2.497357 
P-value...................: 0.0125123 
Hosmer and Lemeshow r^2...: 0.06335 
Cox and Snell r^2.........: 0.07356 
Nagelkerke's pseudo r^2...: 0.10499 
Sample size of AE DB......: 2388 
Sample size of model......: 622 
Missing data %............: 73.9531 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + SmokerCurrent, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale      SmokerCurrentyes  
             -0.1522                0.2498                0.6365                0.3340  

Degrees of Freedom: 619 Total (i.e. Null);  616 Residual
Null Deviance:      835.3 
Residual Deviance: 810.9    AIC: 818.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0278  -1.2499   0.7814   0.9985   1.6067  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                4.328e-01  1.886e+00   0.229 0.818487    
currentDF[, PROTEIN]       2.194e-01  8.702e-02   2.521 0.011704 *  
Age                       -2.754e-03  1.079e-02  -0.255 0.798489    
Gendermale                 6.859e-01  1.848e-01   3.711 0.000206 ***
Hypertension.compositeyes -2.878e-01  2.614e-01  -1.101 0.270823    
DiabetesStatusDiabetes    -1.545e-01  2.130e-01  -0.725 0.468254    
SmokerCurrentyes           3.034e-01  1.908e-01   1.590 0.111766    
Med.Statin.LLDyes         -2.655e-01  2.148e-01  -1.236 0.216525    
Med.all.antiplateletyes    3.741e-01  2.947e-01   1.270 0.204246    
GFR_MDRD                  -4.773e-03  4.840e-03  -0.986 0.324056    
BMI                       -6.643e-03  2.344e-02  -0.283 0.776888    
CAD_history                1.800e-01  2.001e-01   0.899 0.368391    
Stroke_history             1.644e-01  1.892e-01   0.869 0.384946    
Peripheral.interv          4.845e-02  2.214e-01   0.219 0.826790    
stenose50-70%             -1.988e-01  1.475e+00  -0.135 0.892768    
stenose70-90%              8.911e-02  1.438e+00   0.062 0.950580    
stenose90-99%              2.268e-01  1.440e+00   0.158 0.874809    
stenose100% (Occlusion)   -3.762e-01  1.763e+00  -0.213 0.831001    
stenose50-99%              1.457e+01  6.226e+02   0.023 0.981333    
stenose70-99%              1.153e+00  1.844e+00   0.625 0.531866    
hsCRP_plasma              -4.849e-04  7.984e-04  -0.607 0.543636    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 835.34  on 619  degrees of freedom
Residual deviance: 798.71  on 599  degrees of freedom
AIC: 840.71

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.21938 
Standard error............: 0.087023 
Odds ratio (effect size)..: 1.245 
Lower 95% CI..............: 1.05 
Upper 95% CI..............: 1.477 
Z-value...................: 2.520944 
P-value...................: 0.01170406 
Hosmer and Lemeshow r^2...: 0.04385 
Cox and Snell r^2.........: 0.057369 
Nagelkerke's pseudo r^2...: 0.077519 
Sample size of AE DB......: 2388 
Sample size of model......: 620 
Missing data %............: 74.03685 

Analysis of MCP1_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + SmokerCurrent + Peripheral.interv + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age         SmokerCurrentyes        Peripheral.interv  
               -2.39988                 -0.44033                  0.02181                  0.26603                 -0.41214  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%  
                0.10233                  0.80161                  0.93392                  1.61242                -14.94601  
          stenose70-99%  
              -14.58139  

Degrees of Freedom: 637 Total (i.e. Null);  627 Residual
Null Deviance:      882.6 
Residual Deviance: 828.8    AIC: 850.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7296  -1.0748  -0.6531   1.1213   1.9692  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)               -3.185e+00  1.759e+00  -1.811   0.0701 .  
currentDF[, PROTEIN]      -4.475e-01  8.767e-02  -5.104 3.32e-07 ***
Age                        2.189e-02  1.062e-02   2.061   0.0393 *  
Gendermale                 4.745e-02  1.830e-01   0.259   0.7954    
Hypertension.compositeyes  2.459e-01  2.551e-01   0.964   0.3351    
DiabetesStatusDiabetes    -1.058e-01  2.126e-01  -0.498   0.6186    
SmokerCurrentyes           2.913e-01  1.842e-01   1.582   0.1137    
Med.Statin.LLDyes         -2.958e-01  2.055e-01  -1.440   0.1500    
Med.all.antiplateletyes    1.271e-01  2.933e-01   0.434   0.6646    
GFR_MDRD                   4.087e-03  4.747e-03   0.861   0.3893    
BMI                        9.852e-03  2.247e-02   0.438   0.6611    
CAD_history                2.717e-01  1.945e-01   1.397   0.1625    
Stroke_history             1.043e-01  1.845e-01   0.565   0.5719    
Peripheral.interv         -4.251e-01  2.167e-01  -1.962   0.0498 *  
stenose50-70%              8.138e-02  1.294e+00   0.063   0.9498    
stenose70-90%              8.083e-01  1.243e+00   0.650   0.5156    
stenose90-99%              9.315e-01  1.243e+00   0.749   0.4536    
stenose100% (Occlusion)    1.628e+00  1.713e+00   0.950   0.3420    
stenose50-99%             -1.493e+01  9.904e+02  -0.015   0.9880    
stenose70-99%             -1.457e+01  6.868e+02  -0.021   0.9831    
hsCRP_plasma              -1.953e-04  8.261e-04  -0.236   0.8131    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 882.64  on 637  degrees of freedom
Residual deviance: 822.45  on 617  degrees of freedom
AIC: 864.45

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.447486 
Standard error............: 0.087672 
Odds ratio (effect size)..: 0.639 
Lower 95% CI..............: 0.538 
Upper 95% CI..............: 0.759 
Z-value...................: -5.104117 
P-value...................: 3.323428e-07 
Hosmer and Lemeshow r^2...: 0.068196 
Cox and Snell r^2.........: 0.090032 
Nagelkerke's pseudo r^2...: 0.120157 
Sample size of AE DB......: 2388 
Sample size of model......: 638 
Missing data %............: 73.28308 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerCurrent + Med.all.antiplatelet + CAD_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]         SmokerCurrentyes  Med.all.antiplateletyes              CAD_history  
                 0.5805                  -0.2561                   0.4792                   0.4410                   0.5331  

Degrees of Freedom: 640 Total (i.e. Null);  636 Residual
Null Deviance:      683 
Residual Deviance: 663.3    AIC: 673.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3079   0.3988   0.6302   0.7500   1.1886  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.304e+01  8.257e+02   0.016   0.9874  
currentDF[, PROTEIN]      -2.416e-01  9.930e-02  -2.433   0.0150 *
Age                        7.120e-03  1.214e-02   0.586   0.5577  
Gendermale                 1.621e-01  2.111e-01   0.768   0.4425  
Hypertension.compositeyes  6.908e-02  2.849e-01   0.243   0.8084  
DiabetesStatusDiabetes     1.106e-01  2.528e-01   0.438   0.6616  
SmokerCurrentyes           5.456e-01  2.252e-01   2.423   0.0154 *
Med.Statin.LLDyes         -7.351e-02  2.379e-01  -0.309   0.7574  
Med.all.antiplateletyes    4.861e-01  3.204e-01   1.517   0.1293  
GFR_MDRD                   2.182e-03  5.484e-03   0.398   0.6908  
BMI                        3.061e-02  2.736e-02   1.119   0.2632  
CAD_history                5.312e-01  2.407e-01   2.207   0.0273 *
Stroke_history             2.544e-01  2.187e-01   1.163   0.2447  
Peripheral.interv          1.899e-01  2.604e-01   0.729   0.4658  
stenose50-70%             -1.411e+01  8.257e+02  -0.017   0.9864  
stenose70-90%             -1.426e+01  8.257e+02  -0.017   0.9862  
stenose90-99%             -1.418e+01  8.257e+02  -0.017   0.9863  
stenose100% (Occlusion)    1.502e-01  1.086e+03   0.000   0.9999  
stenose50-99%             -2.239e-01  1.316e+03   0.000   0.9999  
stenose70-99%             -1.432e+01  8.257e+02  -0.017   0.9862  
hsCRP_plasma              -7.554e-04  7.479e-04  -1.010   0.3125  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 682.96  on 640  degrees of freedom
Residual deviance: 653.34  on 620  degrees of freedom
AIC: 695.34

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.241606 
Standard error............: 0.099301 
Odds ratio (effect size)..: 0.785 
Lower 95% CI..............: 0.646 
Upper 95% CI..............: 0.954 
Z-value...................: -2.433053 
P-value...................: 0.01497212 
Hosmer and Lemeshow r^2...: 0.043362 
Cox and Snell r^2.........: 0.04515 
Nagelkerke's pseudo r^2...: 0.068885 
Sample size of AE DB......: 2388 
Sample size of model......: 641 
Missing data %............: 73.15745 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale        Stroke_history     Peripheral.interv  
              0.2756                0.2211                0.8570                0.3349               -0.3642  

Degrees of Freedom: 640 Total (i.e. Null);  636 Residual
Null Deviance:      779.2 
Residual Deviance: 743  AIC: 753

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0590  -1.1830   0.6686   0.8340   1.5657  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.421e+01  8.368e+02   0.017   0.9865    
currentDF[, PROTEIN]       2.300e-01  9.342e-02   2.462   0.0138 *  
Age                        6.612e-03  1.139e-02   0.581   0.5615    
Gendermale                 8.980e-01  1.913e-01   4.695 2.67e-06 ***
Hypertension.compositeyes  1.628e-01  2.722e-01   0.598   0.5498    
DiabetesStatusDiabetes    -5.155e-02  2.282e-01  -0.226   0.8213    
SmokerCurrentyes           7.727e-02  2.002e-01   0.386   0.6996    
Med.Statin.LLDyes         -7.564e-02  2.280e-01  -0.332   0.7401    
Med.all.antiplateletyes    8.708e-02  3.168e-01   0.275   0.7834    
GFR_MDRD                  -1.562e-04  5.212e-03  -0.030   0.9761    
BMI                        4.533e-03  2.369e-02   0.191   0.8483    
CAD_history               -1.616e-01  2.093e-01  -0.772   0.4402    
Stroke_history             3.225e-01  2.074e-01   1.555   0.1199    
Peripheral.interv         -3.953e-01  2.238e-01  -1.766   0.0773 .  
stenose50-70%             -1.508e+01  8.368e+02  -0.018   0.9856    
stenose70-90%             -1.464e+01  8.368e+02  -0.017   0.9860    
stenose90-99%             -1.456e+01  8.368e+02  -0.017   0.9861    
stenose100% (Occlusion)   -1.569e+01  8.368e+02  -0.019   0.9850    
stenose50-99%             -3.120e+01  1.302e+03  -0.024   0.9809    
stenose70-99%             -1.570e+01  8.368e+02  -0.019   0.9850    
hsCRP_plasma               9.345e-04  1.300e-03   0.719   0.4721    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 779.19  on 640  degrees of freedom
Residual deviance: 729.62  on 620  degrees of freedom
AIC: 771.62

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.229996 
Standard error............: 0.093417 
Odds ratio (effect size)..: 1.259 
Lower 95% CI..............: 1.048 
Upper 95% CI..............: 1.511 
Z-value...................: 2.462037 
P-value...................: 0.01381502 
Hosmer and Lemeshow r^2...: 0.063619 
Cox and Snell r^2.........: 0.07442 
Nagelkerke's pseudo r^2...: 0.105791 
Sample size of AE DB......: 2388 
Sample size of model......: 641 
Missing data %............: 73.15745 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)               Gendermale         SmokerCurrentyes        Med.Statin.LLDyes  Med.all.antiplateletyes  
                -0.2358                   0.6317                   0.4079                  -0.4060                   0.4192  

Degrees of Freedom: 638 Total (i.e. Null);  634 Residual
Null Deviance:      861.2 
Residual Deviance: 838.9    AIC: 848.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9749  -1.2424   0.7969   1.0058   1.5314  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                8.622e-01  1.750e+00   0.493 0.622313    
currentDF[, PROTEIN]      -6.833e-02  8.480e-02  -0.806 0.420364    
Age                       -4.438e-03  1.057e-02  -0.420 0.674652    
Gendermale                 6.920e-01  1.823e-01   3.796 0.000147 ***
Hypertension.compositeyes -3.447e-01  2.594e-01  -1.329 0.183993    
DiabetesStatusDiabetes    -1.860e-01  2.087e-01  -0.891 0.372807    
SmokerCurrentyes           3.582e-01  1.871e-01   1.914 0.055564 .  
Med.Statin.LLDyes         -4.437e-01  2.115e-01  -2.097 0.035959 *  
Med.all.antiplateletyes    3.849e-01  2.892e-01   1.331 0.183178    
GFR_MDRD                  -5.926e-03  4.758e-03  -1.246 0.212891    
BMI                        3.031e-03  2.235e-02   0.136 0.892158    
CAD_history                1.916e-01  1.949e-01   0.983 0.325626    
Stroke_history             2.615e-01  1.877e-01   1.393 0.163546    
Peripheral.interv          1.459e-01  2.169e-01   0.673 0.500968    
stenose50-70%             -6.404e-01  1.287e+00  -0.498 0.618782    
stenose70-90%             -2.939e-01  1.246e+00  -0.236 0.813503    
stenose90-99%             -1.048e-01  1.245e+00  -0.084 0.932958    
stenose100% (Occlusion)   -8.639e-01  1.609e+00  -0.537 0.591219    
stenose50-99%              1.414e+01  6.240e+02   0.023 0.981926    
stenose70-99%              5.429e-01  1.702e+00   0.319 0.749796    
hsCRP_plasma              -5.275e-04  7.988e-04  -0.660 0.509031    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 861.23  on 638  degrees of freedom
Residual deviance: 825.59  on 618  degrees of freedom
AIC: 867.59

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: -0.068328 
Standard error............: 0.084797 
Odds ratio (effect size)..: 0.934 
Lower 95% CI..............: 0.791 
Upper 95% CI..............: 1.103 
Z-value...................: -0.80579 
P-value...................: 0.4203639 
Hosmer and Lemeshow r^2...: 0.041381 
Cox and Snell r^2.........: 0.054245 
Nagelkerke's pseudo r^2...: 0.073286 
Sample size of AE DB......: 2388 
Sample size of model......: 639 
Missing data %............: 73.24121 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 5

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, stenosis, and IL6 in plaques.

Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_LN, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_LN.

- processing Macrophages_LN
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(COVARIATES_M5)` instead of `COVARIATES_M5` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.

Call:
lm(formula = currentDF[, PROTEIN] ~ IL6_pg_ug_2015_LN, data = currentDF)

Coefficients:
      (Intercept)  IL6_pg_ug_2015_LN  
            4.611              0.185  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4165 -0.8007  0.0170  0.6097  3.9655 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                5.927383   1.271370   4.662 4.78e-06 ***
currentDF[, TRAIT]        -0.021467   0.030894  -0.695    0.488    
Age                       -0.001101   0.008324  -0.132    0.895    
Gendermale                -0.138367   0.141507  -0.978    0.329    
Hypertension.compositeyes -0.050626   0.198464  -0.255    0.799    
DiabetesStatusDiabetes     0.083022   0.160017   0.519    0.604    
SmokerCurrentyes          -0.060466   0.140820  -0.429    0.668    
Med.Statin.LLDyes         -0.227598   0.142187  -1.601    0.111    
Med.all.antiplateletyes   -0.169649   0.215412  -0.788    0.432    
GFR_MDRD                   0.004727   0.003655   1.293    0.197    
BMI                       -0.007313   0.017749  -0.412    0.681    
CAD_history                0.094211   0.138611   0.680    0.497    
Stroke_history             0.075899   0.130234   0.583    0.560    
Peripheral.interv          0.109728   0.154872   0.709    0.479    
stenose50-70%             -0.898824   0.820875  -1.095    0.274    
stenose70-90%             -1.030382   0.767061  -1.343    0.180    
stenose90-99%             -1.011916   0.762658  -1.327    0.186    
stenose100% (Occlusion)   -1.493765   1.019983  -1.464    0.144    
IL6_pg_ug_2015_LN          0.187771   0.040747   4.608 6.10e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.06 on 289 degrees of freedom
Multiple R-squared:  0.09795,   Adjusted R-squared:  0.04177 
F-statistic: 1.743 on 18 and 289 DF,  p-value: 0.03197

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: -0.021467 
Standard error............: 0.030894 
Odds ratio (effect size)..: 0.979 
Lower 95% CI..............: 0.921 
Upper 95% CI..............: 1.04 
T-value...................: -0.694856 
P-value...................: 0.4877039 
R^2.......................: 0.097948 
Adjusted r^2..............: 0.041765 
Sample size of AE DB......: 2388 
Sample size of model......: 308 
Missing data %............: 87.10218 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD + IL6_pg_ug_2015_LN, 
    data = currentDF)

Coefficients:
      (Intercept)  Med.Statin.LLDyes  IL6_pg_ug_2015_LN  
           4.6808            -0.1892             0.1643  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.4525 -0.7612  0.0033  0.6267  3.9346 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                6.181174   1.230408   5.024 8.78e-07 ***
currentDF[, TRAIT]         0.023514   0.039255   0.599    0.550    
Age                       -0.002751   0.008070  -0.341    0.733    
Gendermale                -0.155936   0.137818  -1.131    0.259    
Hypertension.compositeyes -0.054517   0.188258  -0.290    0.772    
DiabetesStatusDiabetes     0.049238   0.154631   0.318    0.750    
SmokerCurrentyes          -0.086609   0.135319  -0.640    0.523    
Med.Statin.LLDyes         -0.241771   0.137175  -1.763    0.079 .  
Med.all.antiplateletyes   -0.221620   0.208851  -1.061    0.289    
GFR_MDRD                   0.004772   0.003605   1.324    0.187    
BMI                       -0.009630   0.016987  -0.567    0.571    
CAD_history                0.080537   0.136960   0.588    0.557    
Stroke_history             0.039877   0.126630   0.315    0.753    
Peripheral.interv          0.134259   0.154797   0.867    0.386    
stenose50-70%             -0.929859   0.809564  -1.149    0.252    
stenose70-90%             -1.038246   0.756292  -1.373    0.171    
stenose90-99%             -1.015694   0.752079  -1.351    0.178    
stenose100% (Occlusion)   -1.518787   1.002792  -1.515    0.131    
IL6_pg_ug_2015_LN          0.171314   0.039323   4.357 1.82e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.046 on 296 degrees of freedom
Multiple R-squared:  0.09119,   Adjusted R-squared:  0.03592 
F-statistic:  1.65 on 18 and 296 DF,  p-value: 0.04768

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: 0.023514 
Standard error............: 0.039255 
Odds ratio (effect size)..: 1.024 
Lower 95% CI..............: 0.948 
Upper 95% CI..............: 1.106 
T-value...................: 0.59901 
P-value...................: 0.5496245 
R^2.......................: 0.091189 
Adjusted r^2..............: 0.035923 
Sample size of AE DB......: 2388 
Sample size of model......: 315 
Missing data %............: 86.80904 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD + IL6_pg_ug_2015_LN, 
    data = currentDF)

Coefficients:
      (Intercept)           GFR_MDRD  IL6_pg_ug_2015_LN  
         4.194942           0.004928           0.165697  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3449 -0.7867 -0.0304  0.6422  4.0349 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                6.225614   1.280705   4.861 1.92e-06 ***
currentDF[, TRAIT]        -0.096433   0.098811  -0.976    0.330    
Age                       -0.003013   0.008161  -0.369    0.712    
Gendermale                -0.127779   0.139441  -0.916    0.360    
Hypertension.compositeyes -0.004603   0.195515  -0.024    0.981    
DiabetesStatusDiabetes     0.088105   0.160926   0.547    0.584    
SmokerCurrentyes          -0.057679   0.139138  -0.415    0.679    
Med.Statin.LLDyes         -0.228912   0.139883  -1.636    0.103    
Med.all.antiplateletyes   -0.219084   0.214922  -1.019    0.309    
GFR_MDRD                   0.005865   0.003695   1.587    0.114    
BMI                       -0.010273   0.017341  -0.592    0.554    
CAD_history                0.108852   0.140247   0.776    0.438    
Stroke_history             0.077217   0.129761   0.595    0.552    
Peripheral.interv          0.155096   0.157608   0.984    0.326    
stenose50-70%             -0.931291   0.820260  -1.135    0.257    
stenose70-90%             -1.066183   0.767461  -1.389    0.166    
stenose90-99%             -1.047425   0.762490  -1.374    0.171    
stenose100% (Occlusion)   -1.542241   1.019750  -1.512    0.132    
IL6_pg_ug_2015_LN          0.169171   0.039667   4.265 2.71e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.059 on 289 degrees of freedom
Multiple R-squared:  0.09279,   Adjusted R-squared:  0.03629 
F-statistic: 1.642 on 18 and 289 DF,  p-value: 0.04945

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.096433 
Standard error............: 0.098811 
Odds ratio (effect size)..: 0.908 
Lower 95% CI..............: 0.748 
Upper 95% CI..............: 1.102 
T-value...................: -0.975931 
P-value...................: 0.3299147 
R^2.......................: 0.092794 
Adjusted r^2..............: 0.03629 
Sample size of AE DB......: 2388 
Sample size of model......: 308 
Missing data %............: 87.10218 

Analysis of MCP1_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + DiabetesStatus + 
    Med.Statin.LLD + CAD_history + Peripheral.interv + IL6_pg_ug_2015_LN, 
    data = currentDF)

Coefficients:
           (Intercept)      currentDF[, TRAIT]  DiabetesStatusDiabetes       Med.Statin.LLDyes             CAD_history  
               5.42282                 0.03618                -0.19017                -0.21111                 0.18964  
     Peripheral.interv       IL6_pg_ug_2015_LN  
              -0.19098                 0.17093  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.0894 -0.5107  0.0377  0.6253  2.1622 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                6.5922102  0.8650138   7.621 2.24e-13 ***
currentDF[, TRAIT]         0.0325660  0.0220355   1.478   0.1403    
Age                       -0.0083277  0.0054360  -1.532   0.1264    
Gendermale                 0.1385775  0.0943481   1.469   0.1428    
Hypertension.compositeyes -0.1510837  0.1290660  -1.171   0.2425    
DiabetesStatusDiabetes    -0.1665340  0.1070314  -1.556   0.1206    
SmokerCurrentyes          -0.0779224  0.0928753  -0.839   0.4020    
Med.Statin.LLDyes         -0.2435613  0.0983058  -2.478   0.0137 *  
Med.all.antiplateletyes    0.1077614  0.1519566   0.709   0.4787    
GFR_MDRD                  -0.0009545  0.0023944  -0.399   0.6904    
BMI                       -0.0130165  0.0117355  -1.109   0.2681    
CAD_history                0.1775369  0.0964592   1.841   0.0665 .  
Stroke_history             0.0196930  0.0890960   0.221   0.8252    
Peripheral.interv         -0.1903656  0.1107707  -1.719   0.0866 .  
stenose50-70%             -0.2004232  0.6205990  -0.323   0.7469    
stenose70-90%             -0.2165956  0.5830618  -0.371   0.7105    
stenose90-99%             -0.1922047  0.5805857  -0.331   0.7408    
stenose100% (Occlusion)   -1.2943097  0.7227984  -1.791   0.0742 .  
IL6_pg_ug_2015_LN          0.1735288  0.0274057   6.332 7.20e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.81 on 361 degrees of freedom
Multiple R-squared:  0.1632,    Adjusted R-squared:  0.1215 
F-statistic: 3.911 on 18 and 361 DF,  p-value: 2.367e-07

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0.032566 
Standard error............: 0.022036 
Odds ratio (effect size)..: 1.033 
Lower 95% CI..............: 0.989 
Upper 95% CI..............: 1.079 
T-value...................: 1.477887 
P-value...................: 0.1403099 
R^2.......................: 0.163189 
Adjusted r^2..............: 0.121464 
Sample size of AE DB......: 2388 
Sample size of model......: 380 
Missing data %............: 84.0871 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + Med.Statin.LLD + 
    Med.all.antiplatelet + CAD_history + Peripheral.interv + 
    IL6_pg_ug_2015_LN, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale  Hypertension.compositeyes  
                  5.82157                   -0.08187                   -0.01093                    0.14979                   -0.16992  
   DiabetesStatusDiabetes          Med.Statin.LLDyes    Med.all.antiplateletyes                CAD_history          Peripheral.interv  
                 -0.18956                   -0.17667                    0.23118                    0.22428                   -0.16555  
        IL6_pg_ug_2015_LN  
                  0.14869  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.06735 -0.55452 -0.00776  0.57409  2.18813 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                6.681140   0.844925   7.907 3.10e-14 ***
currentDF[, TRAIT]        -0.082710   0.026881  -3.077  0.00225 ** 
Age                       -0.013151   0.005325  -2.470  0.01397 *  
Gendermale                 0.142034   0.092428   1.537  0.12523    
Hypertension.compositeyes -0.182526   0.123943  -1.473  0.14170    
DiabetesStatusDiabetes    -0.180275   0.104594  -1.724  0.08562 .  
SmokerCurrentyes          -0.086804   0.089914  -0.965  0.33497    
Med.Statin.LLDyes         -0.194348   0.095049  -2.045  0.04159 *  
Med.all.antiplateletyes    0.140214   0.147589   0.950  0.34272    
GFR_MDRD                  -0.001075   0.002351  -0.457  0.64778    
BMI                       -0.011283   0.011311  -0.998  0.31917    
CAD_history                0.208520   0.095341   2.187  0.02936 *  
Stroke_history             0.038319   0.086917   0.441  0.65957    
Peripheral.interv         -0.182971   0.109885  -1.665  0.09674 .  
stenose50-70%             -0.144584   0.612292  -0.236  0.81346    
stenose70-90%             -0.199810   0.575211  -0.347  0.72851    
stenose90-99%             -0.173815   0.572786  -0.303  0.76171    
stenose100% (Occlusion)   -1.254017   0.712227  -1.761  0.07912 .  
IL6_pg_ug_2015_LN          0.150253   0.026926   5.580 4.67e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.7993 on 368 degrees of freedom
Multiple R-squared:  0.1799,    Adjusted R-squared:  0.1398 
F-statistic: 4.485 on 18 and 368 DF,  p-value: 7.585e-09

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.08271 
Standard error............: 0.026881 
Odds ratio (effect size)..: 0.921 
Lower 95% CI..............: 0.873 
Upper 95% CI..............: 0.97 
T-value...................: -3.076904 
P-value...................: 0.002248252 
R^2.......................: 0.179909 
Adjusted r^2..............: 0.139796 
Sample size of AE DB......: 2388 
Sample size of model......: 387 
Missing data %............: 83.79397 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + DiabetesStatus + Med.Statin.LLD + Med.all.antiplatelet + 
    CAD_history + IL6_pg_ug_2015_LN, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age               Gendermale   DiabetesStatusDiabetes  
               5.662582                -0.102797                -0.007737                 0.233123                -0.157455  
      Med.Statin.LLDyes  Med.all.antiplateletyes              CAD_history        IL6_pg_ug_2015_LN  
              -0.194810                 0.255192                 0.196574                 0.172016  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.3634 -0.5541  0.0188  0.6013  2.2016 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                6.619759   0.872874   7.584 2.89e-13 ***
currentDF[, TRAIT]        -0.115158   0.063779  -1.806   0.0718 .  
Age                       -0.009234   0.005326  -1.734   0.0838 .  
Gendermale                 0.217503   0.093097   2.336   0.0200 *  
Hypertension.compositeyes -0.154299   0.128341  -1.202   0.2301    
DiabetesStatusDiabetes    -0.137882   0.106947  -1.289   0.1981    
SmokerCurrentyes          -0.098746   0.091868  -1.075   0.2832    
Med.Statin.LLDyes         -0.201521   0.096729  -2.083   0.0379 *  
Med.all.antiplateletyes    0.159414   0.150841   1.057   0.2913    
GFR_MDRD                  -0.000691   0.002419  -0.286   0.7753    
BMI                       -0.011145   0.011563  -0.964   0.3358    
CAD_history                0.207952   0.097714   2.128   0.0340 *  
Stroke_history             0.041639   0.089080   0.467   0.6405    
Peripheral.interv         -0.138894   0.112537  -1.234   0.2179    
stenose50-70%             -0.225423   0.617761  -0.365   0.7154    
stenose70-90%             -0.213750   0.580972  -0.368   0.7132    
stenose90-99%             -0.178468   0.578172  -0.309   0.7577    
stenose100% (Occlusion)   -1.264080   0.720276  -1.755   0.0801 .  
IL6_pg_ug_2015_LN          0.174170   0.026779   6.504 2.62e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8061 on 360 degrees of freedom
Multiple R-squared:  0.1713,    Adjusted R-squared:  0.1299 
F-statistic: 4.135 on 18 and 360 DF,  p-value: 6.343e-08

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.115159 
Standard error............: 0.063779 
Odds ratio (effect size)..: 0.891 
Lower 95% CI..............: 0.786 
Upper 95% CI..............: 1.01 
T-value...................: -1.805579 
P-value...................: 0.07181944 
R^2.......................: 0.171328 
Adjusted r^2..............: 0.129895 
Sample size of AE DB......: 2388 
Sample size of model......: 379 
Missing data %............: 84.12898 

Analysis of IL6_pg_ug_2015_LN.

- processing Macrophages_LN
attempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Hypertension.composite + SmokerCurrent + Med.Statin.LLD + 
    Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + 
    Peripheral.interv + stenose + IL6_pg_ug_2015_LN, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age  Hypertension.compositeyes           SmokerCurrentyes  
                        0                          0                          0                          0                          0  
        Med.Statin.LLDyes    Med.all.antiplateletyes                   GFR_MDRD                        BMI                CAD_history  
                        0                          0                          0                          0                          0  
           Stroke_history          Peripheral.interv              stenose50-70%              stenose70-90%              stenose90-99%  
                        0                          0                          0                          0                          0  
  stenose100% (Occlusion)              stenose50-99%              stenose70-99%          IL6_pg_ug_2015_LN  
                        0                          0                          0                          1  
essentially perfect fit: summary may be unreliable

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
       Min         1Q     Median         3Q        Max 
-9.364e-16 -2.744e-17  1.700e-19  2.931e-17  2.894e-16 

Coefficients:
                           Estimate Std. Error   t value Pr(>|t|)    
(Intercept)               0.000e+00  4.875e-17 0.000e+00        1    
currentDF[, TRAIT]        0.000e+00  1.348e-18 0.000e+00        1    
Age                       0.000e+00  3.016e-19 0.000e+00        1    
Gendermale                0.000e+00  5.374e-18 0.000e+00        1    
Hypertension.compositeyes 0.000e+00  7.505e-18 0.000e+00        1    
DiabetesStatusDiabetes    0.000e+00  5.875e-18 0.000e+00        1    
SmokerCurrentyes          0.000e+00  5.417e-18 0.000e+00        1    
Med.Statin.LLDyes         0.000e+00  5.946e-18 0.000e+00        1    
Med.all.antiplateletyes   0.000e+00  8.002e-18 0.000e+00        1    
GFR_MDRD                  0.000e+00  1.280e-19 0.000e+00        1    
BMI                       0.000e+00  6.748e-19 0.000e+00        1    
CAD_history               0.000e+00  5.515e-18 0.000e+00        1    
Stroke_history            0.000e+00  5.200e-18 0.000e+00        1    
Peripheral.interv         0.000e+00  6.415e-18 0.000e+00        1    
stenose50-70%             0.000e+00  3.494e-17 0.000e+00        1    
stenose70-90%             0.000e+00  3.375e-17 0.000e+00        1    
stenose90-99%             0.000e+00  3.374e-17 0.000e+00        1    
stenose100% (Occlusion)   0.000e+00  4.412e-17 0.000e+00        1    
stenose50-99%             0.000e+00  6.266e-17 0.000e+00        1    
stenose70-99%             0.000e+00  4.545e-17 0.000e+00        1    
IL6_pg_ug_2015_LN         1.000e+00  1.682e-18 5.944e+17   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 7.474e-17 on 955 degrees of freedom
Multiple R-squared:      1, Adjusted R-squared:      1 
F-statistic: 1.857e+34 on 20 and 955 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Macrophages_LN ' .
essentially perfect fit: summary may be unreliable
Collecting data.
essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliable
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0 
Standard error............: 0 
Odds ratio (effect size)..: 1 
Lower 95% CI..............: 1 
Upper 95% CI..............: 1 
T-value...................: 0 
P-value...................: 1 
R^2.......................: 1 
Adjusted r^2..............: 1 
Sample size of AE DB......: 2388 
Sample size of model......: 976 
Missing data %............: 59.12898 

- processing SMC_LN
attempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + 
    BMI + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_LN + 
    CAD_history, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age   DiabetesStatusDiabetes         SmokerCurrentyes  
              0.000e+00                0.000e+00                0.000e+00                0.000e+00                0.000e+00  
      Med.Statin.LLDyes  Med.all.antiplateletyes                      BMI           Stroke_history        Peripheral.interv  
              0.000e+00                0.000e+00                0.000e+00                0.000e+00                0.000e+00  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%  
              0.000e+00                0.000e+00                0.000e+00                0.000e+00                0.000e+00  
          stenose70-99%        IL6_pg_ug_2015_LN              CAD_history  
              0.000e+00                1.000e+00               -7.715e-18  
essentially perfect fit: summary may be unreliable

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
       Min         1Q     Median         3Q        Max 
-9.157e-15 -3.370e-17  6.900e-18  4.980e-17  4.712e-16 

Coefficients:
                           Estimate Std. Error   t value Pr(>|t|)    
(Intercept)               0.000e+00  1.998e-16 0.000e+00        1    
currentDF[, TRAIT]        0.000e+00  6.793e-18 0.000e+00        1    
Age                       0.000e+00  1.239e-18 0.000e+00        1    
Gendermale                0.000e+00  2.212e-17 0.000e+00        1    
Hypertension.compositeyes 0.000e+00  3.043e-17 0.000e+00        1    
DiabetesStatusDiabetes    0.000e+00  2.413e-17 0.000e+00        1    
SmokerCurrentyes          0.000e+00  2.219e-17 0.000e+00        1    
Med.Statin.LLDyes         0.000e+00  2.412e-17 0.000e+00        1    
Med.all.antiplateletyes   0.000e+00  3.275e-17 0.000e+00        1    
GFR_MDRD                  0.000e+00  5.282e-19 0.000e+00        1    
BMI                       0.000e+00  2.745e-18 0.000e+00        1    
CAD_history               0.000e+00  2.271e-17 0.000e+00        1    
Stroke_history            0.000e+00  2.128e-17 0.000e+00        1    
Peripheral.interv         0.000e+00  2.637e-17 0.000e+00        1    
stenose50-70%             0.000e+00  1.438e-16 0.000e+00        1    
stenose70-90%             0.000e+00  1.390e-16 0.000e+00        1    
stenose90-99%             0.000e+00  1.389e-16 0.000e+00        1    
stenose100% (Occlusion)   0.000e+00  1.767e-16 0.000e+00        1    
stenose50-99%             0.000e+00  2.582e-16 0.000e+00        1    
stenose70-99%             0.000e+00  1.872e-16 0.000e+00        1    
IL6_pg_ug_2015_LN         1.000e+00  6.882e-18 1.453e+17   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.077e-16 on 965 degrees of freedom
Multiple R-squared:      1, Adjusted R-squared:      1 
F-statistic: 1.117e+33 on 20 and 965 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' SMC_LN ' .
essentially perfect fit: summary may be unreliable
Collecting data.
essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliable
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: 0 
Standard error............: 0 
Odds ratio (effect size)..: 1 
Lower 95% CI..............: 1 
Upper 95% CI..............: 1 
T-value...................: 0 
P-value...................: 1 
R^2.......................: 1 
Adjusted r^2..............: 1 
Sample size of AE DB......: 2388 
Sample size of model......: 986 
Missing data %............: 58.71022 

- processing VesselDensity_LN
attempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + stenose + IL6_pg_ug_2015_LN, 
    data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale  Hypertension.compositeyes  
                        0                          0                          0                          0                          0  
   DiabetesStatusDiabetes           SmokerCurrentyes          Med.Statin.LLDyes    Med.all.antiplateletyes                   GFR_MDRD  
                        0                          0                          0                          0                          0  
                      BMI                CAD_history             Stroke_history              stenose50-70%              stenose70-90%  
                        0                          0                          0                          0                          0  
            stenose90-99%    stenose100% (Occlusion)              stenose50-99%              stenose70-99%          IL6_pg_ug_2015_LN  
                        0                          0                          0                          0                          1  
essentially perfect fit: summary may be unreliable

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
       Min         1Q     Median         3Q        Max 
-3.969e-16 -2.654e-17  1.430e-18  2.794e-17  3.111e-16 

Coefficients:
                           Estimate Std. Error   t value Pr(>|t|)    
(Intercept)               0.000e+00  4.555e-17 0.000e+00        1    
currentDF[, TRAIT]        0.000e+00  2.672e-18 0.000e+00        1    
Age                       0.000e+00  2.841e-19 0.000e+00        1    
Gendermale                0.000e+00  5.085e-18 0.000e+00        1    
Hypertension.compositeyes 0.000e+00  7.075e-18 0.000e+00        1    
DiabetesStatusDiabetes    0.000e+00  5.696e-18 0.000e+00        1    
SmokerCurrentyes          0.000e+00  5.167e-18 0.000e+00        1    
Med.Statin.LLDyes         0.000e+00  5.575e-18 0.000e+00        1    
Med.all.antiplateletyes   0.000e+00  7.841e-18 0.000e+00        1    
GFR_MDRD                  0.000e+00  1.234e-19 0.000e+00        1    
BMI                       0.000e+00  6.333e-19 0.000e+00        1    
CAD_history               0.000e+00  5.331e-18 0.000e+00        1    
Stroke_history            0.000e+00  4.953e-18 0.000e+00        1    
Peripheral.interv         0.000e+00  6.250e-18 0.000e+00        1    
stenose50-70%             0.000e+00  3.205e-17 0.000e+00        1    
stenose70-90%             0.000e+00  3.076e-17 0.000e+00        1    
stenose90-99%             0.000e+00  3.072e-17 0.000e+00        1    
stenose100% (Occlusion)   0.000e+00  4.017e-17 0.000e+00        1    
stenose50-99%             0.000e+00  5.700e-17 0.000e+00        1    
stenose70-99%             0.000e+00  4.583e-17 0.000e+00        1    
IL6_pg_ug_2015_LN         1.000e+00  1.580e-18 6.327e+17   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 6.797e-17 on 869 degrees of freedom
Multiple R-squared:      1, Adjusted R-squared:      1 
F-statistic: 2.083e+34 on 20 and 869 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
essentially perfect fit: summary may be unreliable
Collecting data.
essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliable
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: 0 
Standard error............: 0 
Odds ratio (effect size)..: 1 
Lower 95% CI..............: 1 
Upper 95% CI..............: 1 
T-value...................: 0 
P-value...................: 1 
R^2.......................: 1 
Adjusted r^2..............: 1 
Sample size of AE DB......: 2388 
Sample size of model......: 890 
Missing data %............: 62.73032 

Analysis of IL6R_pg_ug_2015_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Med.Statin.LLD + Peripheral.interv + stenose + IL6_pg_ug_2015_LN, 
    data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age               Gendermale        Med.Statin.LLDyes  
              -1.242877                 0.056355                -0.006051                -0.127290                -0.343429  
      Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
               0.267173                 0.649136                 0.905228                 1.038322                 0.609550  
          stenose50-99%            stenose70-99%        IL6_pg_ug_2015_LN  
               0.884428                -0.025479                 0.285405  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.9558 -0.5150  0.1072  0.6086  2.9779 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -0.966276   0.715026  -1.351  0.17690    
currentDF[, TRAIT]         0.055979   0.018885   2.964  0.00311 ** 
Age                       -0.007873   0.004258  -1.849  0.06476 .  
Gendermale                -0.100562   0.075655  -1.329  0.18411    
Hypertension.compositeyes  0.102874   0.104214   0.987  0.32384    
DiabetesStatusDiabetes    -0.105455   0.082109  -1.284  0.19935    
SmokerCurrentyes          -0.007566   0.075970  -0.100  0.92069    
Med.Statin.LLDyes         -0.342999   0.083058  -4.130 3.96e-05 ***
Med.all.antiplateletyes    0.061685   0.111912   0.551  0.58164    
GFR_MDRD                  -0.002185   0.001823  -1.199  0.23093    
BMI                       -0.006955   0.009654  -0.720  0.47143    
CAD_history               -0.045923   0.077279  -0.594  0.55249    
Stroke_history             0.046251   0.072628   0.637  0.52440    
Peripheral.interv          0.275802   0.090336   3.053  0.00233 ** 
stenose50-70%              0.687748   0.534119   1.288  0.19820    
stenose70-90%              0.943458   0.518762   1.819  0.06929 .  
stenose90-99%              1.071486   0.518557   2.066  0.03908 *  
stenose100% (Occlusion)    0.650093   0.648653   1.002  0.31650    
stenose50-99%              0.877489   0.891309   0.984  0.32513    
stenose70-99%             -0.006258   0.666402  -0.009  0.99251    
IL6_pg_ug_2015_LN          0.282153   0.023495  12.009  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.027 on 922 degrees of freedom
Multiple R-squared:  0.2018,    Adjusted R-squared:  0.1845 
F-statistic: 11.65 on 20 and 922 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: 0.055979 
Standard error............: 0.018885 
Odds ratio (effect size)..: 1.058 
Lower 95% CI..............: 1.019 
Upper 95% CI..............: 1.097 
T-value...................: 2.964161 
P-value...................: 0.00311319 
R^2.......................: 0.201768 
Adjusted r^2..............: 0.184452 
Sample size of AE DB......: 2388 
Sample size of model......: 943 
Missing data %............: 60.51089 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    DiabetesStatus + Med.Statin.LLD + GFR_MDRD + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age   DiabetesStatusDiabetes        Med.Statin.LLDyes  
              -1.112015                 0.097459                -0.006820                -0.119368                -0.333161  
               GFR_MDRD        Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  
              -0.002565                 0.268643                 0.679203                 0.926560                 1.056767  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%        IL6_pg_ug_2015_LN  
               0.534907                 0.747309                -0.034556                 0.301993  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-6.1096 -0.5523  0.0949  0.6268  3.1080 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -1.149126   0.704847  -1.630   0.1034    
currentDF[, TRAIT]         0.095663   0.023010   4.157 3.51e-05 ***
Age                       -0.007165   0.004206  -1.703   0.0888 .  
Gendermale                -0.025751   0.074756  -0.344   0.7306    
Hypertension.compositeyes  0.089848   0.101636   0.884   0.3769    
DiabetesStatusDiabetes    -0.115065   0.081121  -1.418   0.1564    
SmokerCurrentyes          -0.009264   0.074833  -0.124   0.9015    
Med.Statin.LLDyes         -0.334934   0.080984  -4.136 3.86e-05 ***
Med.all.antiplateletyes    0.067913   0.110179   0.616   0.5378    
GFR_MDRD                  -0.002576   0.001810  -1.423   0.1550    
BMI                       -0.002947   0.009437  -0.312   0.7549    
CAD_history               -0.048394   0.076543  -0.632   0.5274    
Stroke_history             0.055612   0.071499   0.778   0.4369    
Peripheral.interv          0.275458   0.089322   3.084   0.0021 ** 
stenose50-70%              0.684789   0.528754   1.295   0.1956    
stenose70-90%              0.937515   0.513760   1.825   0.0683 .  
stenose90-99%              1.063799   0.513562   2.071   0.0386 *  
stenose100% (Occlusion)    0.572933   0.627488   0.913   0.3614    
stenose50-99%              0.751427   0.883539   0.850   0.3953    
stenose70-99%             -0.031342   0.660105  -0.047   0.9621    
IL6_pg_ug_2015_LN          0.299518   0.023167  12.928  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.017 on 932 degrees of freedom
Multiple R-squared:  0.2084,    Adjusted R-squared:  0.1914 
F-statistic: 12.27 on 20 and 932 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: 0.095663 
Standard error............: 0.02301 
Odds ratio (effect size)..: 1.1 
Lower 95% CI..............: 1.052 
Upper 95% CI..............: 1.151 
T-value...................: 4.157498 
P-value...................: 3.514086e-05 
R^2.......................: 0.208431 
Adjusted r^2..............: 0.191445 
Sample size of AE DB......: 2388 
Sample size of model......: 953 
Missing data %............: 60.09213 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    DiabetesStatus + Med.Statin.LLD + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age   DiabetesStatusDiabetes        Med.Statin.LLDyes  
              -1.417139                 0.100429                -0.009354                -0.155244                -0.374452  
      Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
               0.280068                 0.737543                 1.050648                 1.157583                 0.634774  
          stenose50-99%            stenose70-99%        IL6_pg_ug_2015_LN  
               0.845134                 0.159239                 0.287450  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-5.9547 -0.5083  0.0729  0.5954  2.9537 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -1.059091   0.743676  -1.424   0.1548    
currentDF[, TRAIT]         0.101026   0.042011   2.405   0.0164 *  
Age                       -0.011355   0.004466  -2.543   0.0112 *  
Gendermale                -0.077358   0.079545  -0.973   0.3311    
Hypertension.compositeyes  0.106818   0.109344   0.977   0.3289    
DiabetesStatusDiabetes    -0.154746   0.088733  -1.744   0.0815 .  
SmokerCurrentyes          -0.031953   0.080677  -0.396   0.6922    
Med.Statin.LLDyes         -0.375621   0.086700  -4.332 1.65e-05 ***
Med.all.antiplateletyes    0.048492   0.122309   0.396   0.6919    
GFR_MDRD                  -0.002204   0.001966  -1.121   0.2627    
BMI                       -0.006095   0.010100  -0.603   0.5464    
CAD_history               -0.088731   0.083166  -1.067   0.2863    
Stroke_history             0.034438   0.077028   0.447   0.6549    
Peripheral.interv          0.276295   0.097946   2.821   0.0049 ** 
stenose50-70%              0.770678   0.544201   1.416   0.1571    
stenose70-90%              1.081230   0.525369   2.058   0.0399 *  
stenose90-99%              1.183737   0.524757   2.256   0.0243 *  
stenose100% (Occlusion)    0.703847   0.656668   1.072   0.2841    
stenose50-99%              0.897784   0.901672   0.996   0.3197    
stenose70-99%              0.192480   0.738956   0.260   0.7946    
IL6_pg_ug_2015_LN          0.284042   0.024577  11.557  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.039 on 837 degrees of freedom
Multiple R-squared:  0.1997,    Adjusted R-squared:  0.1806 
F-statistic: 10.45 on 20 and 837 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: 0.101026 
Standard error............: 0.042011 
Odds ratio (effect size)..: 1.106 
Lower 95% CI..............: 1.019 
Upper 95% CI..............: 1.201 
T-value...................: 2.404755 
P-value...................: 0.01639953 
R^2.......................: 0.199738 
Adjusted r^2..............: 0.180616 
Sample size of AE DB......: 2388 
Sample size of model......: 858 
Missing data %............: 64.07035 

Analysis of MCP1_pg_ug_2015_LN.

- processing Macrophages_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD + 
    stenose + IL6_pg_ug_2015_LN, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]        Med.Statin.LLDyes            stenose50-70%            stenose70-90%  
               -0.06925                 -0.06816                 -0.14401                  0.41560                  0.37828  
          stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%        IL6_pg_ug_2015_LN  
                0.20462                 -0.82420                  1.06365                  0.78594                  0.43860  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.9520 -0.7606 -0.0173  0.8465  3.4182 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -0.0163763  0.7449520  -0.022 0.982466    
currentDF[, TRAIT]        -0.0688244  0.0205939  -3.342 0.000864 ***
Age                        0.0013390  0.0046147   0.290 0.771754    
Gendermale                 0.0813908  0.0821211   0.991 0.321885    
Hypertension.compositeyes -0.1421067  0.1146687  -1.239 0.215546    
DiabetesStatusDiabetes    -0.0367938  0.0897758  -0.410 0.682015    
SmokerCurrentyes          -0.0469934  0.0827910  -0.568 0.570430    
Med.Statin.LLDyes         -0.1426960  0.0911033  -1.566 0.117607    
Med.all.antiplateletyes   -0.0340903  0.1222730  -0.279 0.780455    
GFR_MDRD                   0.0002204  0.0019577   0.113 0.910382    
BMI                       -0.0020417  0.0103105  -0.198 0.843073    
CAD_history                0.0283634  0.0843530   0.336 0.736759    
Stroke_history             0.0501069  0.0794523   0.631 0.528418    
Peripheral.interv          0.1051598  0.0980445   1.073 0.283735    
stenose50-70%              0.3655695  0.5338315   0.685 0.493634    
stenose70-90%              0.3468000  0.5157343   0.672 0.501467    
stenose90-99%              0.1783612  0.5154914   0.346 0.729417    
stenose100% (Occlusion)   -0.8757534  0.6741363  -1.299 0.194232    
stenose50-99%              1.0443032  0.9573696   1.091 0.275634    
stenose70-99%              0.7383695  0.6944600   1.063 0.287947    
IL6_pg_ug_2015_LN          0.4385550  0.0258285  16.979  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.142 on 954 degrees of freedom
Multiple R-squared:  0.2511,    Adjusted R-squared:  0.2354 
F-statistic: 15.99 on 20 and 954 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Macrophages_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: Macrophages_LN 
Effect size...............: -0.068824 
Standard error............: 0.020594 
Odds ratio (effect size)..: 0.933 
Lower 95% CI..............: 0.897 
Upper 95% CI..............: 0.972 
T-value...................: -3.341973 
P-value...................: 0.000864164 
R^2.......................: 0.251058 
Adjusted r^2..............: 0.235357 
Sample size of AE DB......: 2388 
Sample size of model......: 975 
Missing data %............: 59.17085 

- processing SMC_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Coefficients:
            (Intercept)        Med.Statin.LLDyes            stenose50-70%            stenose70-90%            stenose90-99%  
               0.009487                -0.164150                 0.412654                 0.355694                 0.189719  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%        IL6_pg_ug_2015_LN  
              -0.600048                 1.032517                 0.768769                 0.423669  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.8483 -0.7505 -0.0237  0.8525  3.3474 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -0.0325248  0.7466028  -0.044   0.9653    
currentDF[, TRAIT]        -0.0139722  0.0253879  -0.550   0.5822    
Age                        0.0025893  0.0046357   0.559   0.5766    
Gendermale                 0.0688454  0.0826644   0.833   0.4051    
Hypertension.compositeyes -0.1507656  0.1136938  -1.326   0.1851    
DiabetesStatusDiabetes    -0.0656779  0.0901625  -0.728   0.4665    
SmokerCurrentyes          -0.0363466  0.0829328  -0.438   0.6613    
Med.Statin.LLDyes         -0.1625254  0.0903411  -1.799   0.0723 .  
Med.all.antiplateletyes   -0.0230150  0.1223899  -0.188   0.8509    
GFR_MDRD                   0.0005668  0.0019755   0.287   0.7743    
BMI                       -0.0024383  0.0102576  -0.238   0.8122    
CAD_history                0.0296729  0.0849242   0.349   0.7269    
Stroke_history             0.0269355  0.0795241   0.339   0.7349    
Peripheral.interv          0.1480121  0.0985442   1.502   0.1334    
stenose50-70%              0.3622053  0.5371766   0.674   0.5003    
stenose70-90%              0.3267152  0.5193234   0.629   0.5294    
stenose90-99%              0.1660522  0.5191143   0.320   0.7491    
stenose100% (Occlusion)   -0.6657976  0.6600302  -1.009   0.3134    
stenose50-99%              1.0441546  0.9646197   1.082   0.2793    
stenose70-99%              0.7257635  0.6993507   1.038   0.2996    
IL6_pg_ug_2015_LN          0.4232919  0.0258245  16.391   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.15 on 964 degrees of freedom
Multiple R-squared:  0.2402,    Adjusted R-squared:  0.2244 
F-statistic: 15.23 on 20 and 964 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' SMC_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: SMC_LN 
Effect size...............: -0.013972 
Standard error............: 0.025388 
Odds ratio (effect size)..: 0.986 
Lower 95% CI..............: 0.938 
Upper 95% CI..............: 1.036 
T-value...................: -0.550351 
P-value...................: 0.5822061 
R^2.......................: 0.240151 
Adjusted r^2..............: 0.224387 
Sample size of AE DB......: 2388 
Sample size of model......: 985 
Missing data %............: 58.75209 

- processing VesselDensity_LN


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender + 
    Med.Statin.LLD + IL6_pg_ug_2015_LN, data = currentDF)

Coefficients:
       (Intercept)  currentDF[, TRAIT]          Gendermale   Med.Statin.LLDyes   IL6_pg_ug_2015_LN  
            0.4345             -0.1410              0.1228             -0.1753              0.4234  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_LN, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-3.7823 -0.7462 -0.0609  0.8324  3.4019 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                3.809e-01  7.789e-01   0.489  0.62491    
currentDF[, TRAIT]        -1.340e-01  4.572e-02  -2.931  0.00347 ** 
Age                        1.477e-03  4.863e-03   0.304  0.76142    
Gendermale                 1.221e-01  8.693e-02   1.405  0.16051    
Hypertension.compositeyes -1.576e-01  1.210e-01  -1.303  0.19295    
DiabetesStatusDiabetes    -3.504e-02  9.739e-02  -0.360  0.71912    
SmokerCurrentyes          -4.309e-02  8.835e-02  -0.488  0.62582    
Med.Statin.LLDyes         -1.778e-01  9.557e-02  -1.860  0.06321 .  
Med.all.antiplateletyes    4.854e-02  1.341e-01   0.362  0.71739    
GFR_MDRD                  -6.416e-05  2.113e-03  -0.030  0.97578    
BMI                       -4.015e-03  1.083e-02  -0.371  0.71082    
CAD_history                5.405e-02  9.124e-02   0.592  0.55375    
Stroke_history             3.583e-02  8.467e-02   0.423  0.67226    
Peripheral.interv          8.863e-02  1.069e-01   0.829  0.40716    
stenose50-70%              2.078e-01  5.479e-01   0.379  0.70463    
stenose70-90%              2.034e-01  5.259e-01   0.387  0.69900    
stenose90-99%              7.189e-02  5.252e-01   0.137  0.89115    
stenose100% (Occlusion)   -9.176e-01  6.867e-01  -1.336  0.18184    
stenose50-99%              9.888e-01  9.744e-01   1.015  0.31047    
stenose70-99%              4.743e-01  7.835e-01   0.605  0.54506    
IL6_pg_ug_2015_LN          4.268e-01  2.716e-02  15.715  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.162 on 868 degrees of freedom
Multiple R-squared:   0.25, Adjusted R-squared:  0.2327 
F-statistic: 14.47 on 20 and 868 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' VesselDensity_LN ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: VesselDensity_LN 
Effect size...............: -0.133995 
Standard error............: 0.045718 
Odds ratio (effect size)..: 0.875 
Lower 95% CI..............: 0.8 
Upper 95% CI..............: 0.957 
T-value...................: -2.930908 
P-value...................: 0.00346839 
R^2.......................: 0.250029 
Adjusted r^2..............: 0.232749 
Sample size of AE DB......: 2388 
Sample size of model......: 889 
Missing data %............: 62.77219 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_LN, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Hypertension.composite + DiabetesStatus + GFR_MDRD + Stroke_history, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]  Hypertension.compositeyes     DiabetesStatusDiabetes                   GFR_MDRD  
                  0.12030                    0.21230                    0.63975                   -0.55916                   -0.01326  
           Stroke_history  
                 -0.39735  

Degrees of Freedom: 318 Total (i.e. Null);  313 Residual
Null Deviance:      432.7 
Residual Deviance: 416.8    AIC: 428.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2221  -1.1884   0.7612   1.0022   1.7701  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)  
(Intercept)                0.990445   2.534116   0.391   0.6959  
currentDF[, PROTEIN]       0.227533   0.119111   1.910   0.0561 .
Age                       -0.006221   0.016260  -0.383   0.7020  
Gendermale                -0.392668   0.279558  -1.405   0.1601  
Hypertension.compositeyes  0.733341   0.379484   1.932   0.0533 .
DiabetesStatusDiabetes    -0.504068   0.309402  -1.629   0.1033  
SmokerCurrentyes          -0.143862   0.269962  -0.533   0.5941  
Med.Statin.LLDyes         -0.075632   0.278371  -0.272   0.7859  
Med.all.antiplateletyes    0.370670   0.414175   0.895   0.3708  
GFR_MDRD                  -0.015640   0.007490  -2.088   0.0368 *
BMI                       -0.031275   0.034200  -0.914   0.3605  
CAD_history                0.036519   0.274286   0.133   0.8941  
Stroke_history            -0.420124   0.254378  -1.652   0.0986 .
Peripheral.interv         -0.384648   0.304697  -1.262   0.2068  
stenose50-70%              0.868301   1.568518   0.554   0.5799  
stenose70-90%              0.706068   1.456805   0.485   0.6279  
stenose90-99%              0.323228   1.446476   0.223   0.8232  
stenose100% (Occlusion)    1.180498   1.998297   0.591   0.5547  
IL6_pg_ug_2015_LN         -0.010201   0.080798  -0.126   0.8995  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 432.7  on 318  degrees of freedom
Residual deviance: 408.1  on 300  degrees of freedom
AIC: 446.1

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.227533 
Standard error............: 0.119111 
Odds ratio (effect size)..: 1.255 
Lower 95% CI..............: 0.994 
Upper 95% CI..............: 1.586 
Z-value...................: 1.91025 
P-value...................: 0.05610103 
Hosmer and Lemeshow r^2...: 0.056853 
Cox and Snell r^2.........: 0.074218 
Nagelkerke's pseudo r^2...: 0.099969 
Sample size of AE DB......: 2388 
Sample size of model......: 319 
Missing data %............: 86.64154 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ DiabetesStatus + 
    Med.all.antiplatelet + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)   DiabetesStatusDiabetes  Med.all.antiplateletyes            stenose50-70%            stenose70-90%  
               16.84114                  0.57077                  0.72493                 -0.04498                -16.30025  
          stenose90-99%  stenose100% (Occlusion)  
              -16.48248                  0.72493  

Degrees of Freedom: 318 Total (i.e. Null);  312 Residual
Null Deviance:      338.2 
Residual Deviance: 323.6    AIC: 337.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.00741   0.00022   0.61415   0.76055   1.14093  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.772e+01  2.796e+03   0.006   0.9949  
currentDF[, PROTEIN]       9.851e-02  1.347e-01   0.731   0.4647  
Age                        2.759e-03  1.919e-02   0.144   0.8857  
Gendermale                -3.944e-02  3.230e-01  -0.122   0.9028  
Hypertension.compositeyes  3.110e-01  4.299e-01   0.724   0.4694  
DiabetesStatusDiabetes     7.254e-01  4.068e-01   1.783   0.0746 .
SmokerCurrentyes           3.079e-01  3.221e-01   0.956   0.3391  
Med.Statin.LLDyes         -1.470e-03  3.222e-01  -0.005   0.9964  
Med.all.antiplateletyes    6.842e-01  4.460e-01   1.534   0.1250  
GFR_MDRD                  -9.908e-03  8.779e-03  -1.129   0.2591  
BMI                       -5.029e-02  4.101e-02  -1.226   0.2201  
CAD_history               -5.053e-02  3.195e-01  -0.158   0.8743  
Stroke_history             3.052e-01  3.125e-01   0.977   0.3287  
Peripheral.interv         -4.444e-01  3.400e-01  -1.307   0.1911  
stenose50-70%             -2.758e-02  2.995e+03   0.000   1.0000  
stenose70-90%             -1.623e+01  2.796e+03  -0.006   0.9954  
stenose90-99%             -1.648e+01  2.796e+03  -0.006   0.9953  
stenose100% (Occlusion)    7.061e-01  3.606e+03   0.000   0.9998  
IL6_pg_ug_2015_LN         -6.681e-02  9.415e-02  -0.710   0.4780  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 338.23  on 318  degrees of freedom
Residual deviance: 315.17  on 300  degrees of freedom
AIC: 353.17

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.098509 
Standard error............: 0.134721 
Odds ratio (effect size)..: 1.104 
Lower 95% CI..............: 0.847 
Upper 95% CI..............: 1.437 
Z-value...................: 0.731202 
P-value...................: 0.4646559 
Hosmer and Lemeshow r^2...: 0.068172 
Cox and Snell r^2.........: 0.069732 
Nagelkerke's pseudo r^2...: 0.106682 
Sample size of AE DB......: 2388 
Sample size of model......: 319 
Missing data %............: 86.64154 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite + 
    Stroke_history + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)                 Gendermale  Hypertension.compositeyes             Stroke_history          IL6_pg_ug_2015_LN  
                   0.9404                     0.8368                     0.8895                     0.6832                     0.2639  

Degrees of Freedom: 318 Total (i.e. Null);  314 Residual
Null Deviance:      296.4 
Residual Deviance: 276.5    AIC: 286.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5998   0.3280   0.5023   0.6613   1.3541  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.659e+01  1.693e+03   0.010  0.99218   
currentDF[, PROTEIN]      -1.287e-02  1.496e-01  -0.086  0.93142   
Age                       -1.270e-02  2.136e-02  -0.594  0.55226   
Gendermale                 9.800e-01  3.428e-01   2.859  0.00425 **
Hypertension.compositeyes  8.923e-01  4.375e-01   2.039  0.04140 * 
DiabetesStatusDiabetes    -1.737e-01  4.043e-01  -0.430  0.66748   
SmokerCurrentyes          -5.799e-02  3.613e-01  -0.161  0.87248   
Med.Statin.LLDyes         -1.196e-01  3.675e-01  -0.326  0.74475   
Med.all.antiplateletyes    9.547e-02  5.545e-01   0.172  0.86331   
GFR_MDRD                  -8.596e-03  9.983e-03  -0.861  0.38916   
BMI                        1.285e-02  4.392e-02   0.293  0.76981   
CAD_history               -2.536e-01  3.602e-01  -0.704  0.48136   
Stroke_history             7.146e-01  3.673e-01   1.946  0.05170 . 
Peripheral.interv         -1.081e-01  3.816e-01  -0.283  0.77690   
stenose50-70%             -1.543e+01  1.693e+03  -0.009  0.99273   
stenose70-90%             -1.427e+01  1.693e+03  -0.008  0.99327   
stenose90-99%             -1.452e+01  1.693e+03  -0.009  0.99316   
stenose100% (Occlusion)    5.480e-01  2.137e+03   0.000  0.99980   
IL6_pg_ug_2015_LN          2.491e-01  1.126e-01   2.212  0.02697 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 296.40  on 318  degrees of freedom
Residual deviance: 269.53  on 300  degrees of freedom
AIC: 307.53

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.012874 
Standard error............: 0.149596 
Odds ratio (effect size)..: 0.987 
Lower 95% CI..............: 0.736 
Upper 95% CI..............: 1.324 
Z-value...................: -0.086057 
P-value...................: 0.9314209 
Hosmer and Lemeshow r^2...: 0.090654 
Cox and Snell r^2.........: 0.080781 
Nagelkerke's pseudo r^2...: 0.133498 
Sample size of AE DB......: 2388 
Sample size of model......: 319 
Missing data %............: 86.64154 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite + 
    GFR_MDRD, family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)                 Gendermale  Hypertension.compositeyes                   GFR_MDRD  
                  1.27860                    0.64039                    0.57993                   -0.01649  

Degrees of Freedom: 318 Total (i.e. Null);  315 Residual
Null Deviance:      365.7 
Residual Deviance: 353.2    AIC: 361.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0936  -1.1115   0.6435   0.8045   1.3204  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                13.326245 623.802229   0.021   0.9830  
currentDF[, PROTEIN]       -0.083005   0.127209  -0.653   0.5141  
Age                         0.013635   0.017553   0.777   0.4373  
Gendermale                  0.614092   0.293958   2.089   0.0367 *
Hypertension.compositeyes   0.675576   0.386942   1.746   0.0808 .
DiabetesStatusDiabetes     -0.550357   0.335330  -1.641   0.1007  
SmokerCurrentyes            0.062372   0.307216   0.203   0.8391  
Med.Statin.LLDyes           0.002723   0.308712   0.009   0.9930  
Med.all.antiplateletyes    -0.783958   0.578683  -1.355   0.1755  
GFR_MDRD                   -0.012688   0.008376  -1.515   0.1298  
BMI                         0.036855   0.037293   0.988   0.3230  
CAD_history                 0.021073   0.315139   0.067   0.9467  
Stroke_history              0.106885   0.286648   0.373   0.7092  
Peripheral.interv           0.269576   0.354570   0.760   0.4471  
stenose50-70%             -12.761963 623.798211  -0.020   0.9837  
stenose70-90%             -12.986066 623.797895  -0.021   0.9834  
stenose90-99%             -12.843961 623.797875  -0.021   0.9836  
stenose100% (Occlusion)   -13.670628 623.799581  -0.022   0.9825  
IL6_pg_ug_2015_LN           0.095395   0.091250   1.045   0.2958  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 365.74  on 318  degrees of freedom
Residual deviance: 344.46  on 300  degrees of freedom
AIC: 382.46

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: IPH 
Effect size...............: -0.083005 
Standard error............: 0.127209 
Odds ratio (effect size)..: 0.92 
Lower 95% CI..............: 0.717 
Upper 95% CI..............: 1.181 
Z-value...................: -0.652513 
P-value...................: 0.5140706 
Hosmer and Lemeshow r^2...: 0.058163 
Cox and Snell r^2.........: 0.06451 
Nagelkerke's pseudo r^2...: 0.094554 
Sample size of AE DB......: 2388 
Sample size of model......: 319 
Missing data %............: 86.64154 

Analysis of MCP1_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    DiabetesStatus + GFR_MDRD + Stroke_history + Peripheral.interv, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
           (Intercept)    currentDF[, PROTEIN]  DiabetesStatusDiabetes                GFR_MDRD          Stroke_history  
               2.75963                -0.23220                -0.69021                -0.01253                -0.50821  
     Peripheral.interv  
              -0.56921  

Degrees of Freedom: 390 Total (i.e. Null);  385 Residual
Null Deviance:      529.8 
Residual Deviance: 508.7    AIC: 520.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9390  -1.2075   0.7881   1.0028   1.6679  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)   
(Intercept)                0.615657   2.401219   0.256  0.79765   
currentDF[, PROTEIN]      -0.215684   0.137600  -1.567  0.11700   
Age                        0.015147   0.013814   1.097  0.27285   
Gendermale                -0.205593   0.243600  -0.844  0.39868   
Hypertension.compositeyes  0.471313   0.322171   1.463  0.14349   
DiabetesStatusDiabetes    -0.720366   0.273492  -2.634  0.00844 **
SmokerCurrentyes           0.156046   0.236488   0.660  0.50935   
Med.Statin.LLDyes         -0.077618   0.251245  -0.309  0.75737   
Med.all.antiplateletyes    0.276338   0.391583   0.706  0.48038   
GFR_MDRD                  -0.010076   0.006240  -1.615  0.10635   
BMI                        0.003885   0.029690   0.131  0.89588   
CAD_history               -0.015261   0.250409  -0.061  0.95140   
Stroke_history            -0.555223   0.228526  -2.430  0.01512 * 
Peripheral.interv         -0.560383   0.285195  -1.965  0.04942 * 
stenose50-70%              0.572791   1.638818   0.350  0.72670   
stenose70-90%              0.573134   1.547009   0.370  0.71103   
stenose90-99%              0.260900   1.540455   0.169  0.86551   
stenose100% (Occlusion)    1.170352   2.033339   0.576  0.56490   
IL6_pg_ug_2015_LN          0.033806   0.073081   0.463  0.64366   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 529.80  on 390  degrees of freedom
Residual deviance: 502.25  on 372  degrees of freedom
AIC: 540.25

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.215684 
Standard error............: 0.1376 
Odds ratio (effect size)..: 0.806 
Lower 95% CI..............: 0.615 
Upper 95% CI..............: 1.055 
Z-value...................: -1.567475 
P-value...................: 0.1170036 
Hosmer and Lemeshow r^2...: 0.052 
Cox and Snell r^2.........: 0.068034 
Nagelkerke's pseudo r^2...: 0.091684 
Sample size of AE DB......: 2388 
Sample size of model......: 391 
Missing data %............: 83.62647 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    DiabetesStatus + Med.all.antiplatelet + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]   DiabetesStatusDiabetes  Med.all.antiplateletyes        Peripheral.interv  
              19.836681                -0.605307                 0.496906                 0.945983                -0.497109  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
              -0.362219               -16.187313               -16.664391                -0.008556  

Degrees of Freedom: 390 Total (i.e. Null);  382 Residual
Null Deviance:      393.5 
Residual Deviance: 360.5    AIC: 378.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.37247   0.00035   0.55119   0.69395   1.29272  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.976e+01  2.690e+03   0.007  0.99414   
currentDF[, PROTEIN]      -5.976e-01  1.839e-01  -3.250  0.00115 **
Age                        9.003e-03  1.747e-02   0.515  0.60623   
Gendermale                -9.870e-02  3.054e-01  -0.323  0.74655   
Hypertension.compositeyes  1.539e-01  3.957e-01   0.389  0.69738   
DiabetesStatusDiabetes     5.913e-01  3.780e-01   1.564  0.11779   
SmokerCurrentyes           3.815e-01  3.000e-01   1.272  0.20350   
Med.Statin.LLDyes          6.889e-02  3.025e-01   0.228  0.81986   
Med.all.antiplateletyes    9.329e-01  4.358e-01   2.141  0.03231 * 
GFR_MDRD                  -1.867e-03  7.733e-03  -0.241  0.80920   
BMI                       -3.382e-02  3.913e-02  -0.864  0.38744   
CAD_history                4.504e-02  3.045e-01   0.148  0.88240   
Stroke_history             3.011e-01  2.941e-01   1.024  0.30583   
Peripheral.interv         -5.120e-01  3.361e-01  -1.523  0.12774   
stenose50-70%             -2.787e-01  2.878e+03   0.000  0.99992   
stenose70-90%             -1.612e+01  2.690e+03  -0.006  0.99522   
stenose90-99%             -1.663e+01  2.690e+03  -0.006  0.99507   
stenose100% (Occlusion)    1.404e-02  3.236e+03   0.000  1.00000   
IL6_pg_ug_2015_LN         -1.417e-02  9.018e-02  -0.157  0.87517   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 393.52  on 390  degrees of freedom
Residual deviance: 355.78  on 372  degrees of freedom
AIC: 393.78

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.597578 
Standard error............: 0.183866 
Odds ratio (effect size)..: 0.55 
Lower 95% CI..............: 0.384 
Upper 95% CI..............: 0.789 
Z-value...................: -3.250077 
P-value...................: 0.001153738 
Hosmer and Lemeshow r^2...: 0.095909 
Cox and Snell r^2.........: 0.092015 
Nagelkerke's pseudo r^2...: 0.145023 
Sample size of AE DB......: 2388 
Sample size of model......: 391 
Missing data %............: 83.62647 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Hypertension.composite + BMI + Stroke_history + 
    IL6_pg_ug_2015_LN, family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]                 Gendermale  Hypertension.compositeyes                        BMI  
                 -3.32554                    0.60420                    0.82335                    0.68998                    0.05451  
           Stroke_history          IL6_pg_ug_2015_LN  
                  0.64154                    0.17450  

Degrees of Freedom: 390 Total (i.e. Null);  384 Residual
Null Deviance:      367.5 
Residual Deviance: 326.7    AIC: 340.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.6873   0.2956   0.4470   0.6240   1.7142  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                9.920e+00  9.783e+02   0.010 0.991909    
currentDF[, PROTEIN]       6.236e-01  1.805e-01   3.455 0.000549 ***
Age                        6.051e-04  1.827e-02   0.033 0.973581    
Gendermale                 8.943e-01  3.020e-01   2.961 0.003065 ** 
Hypertension.compositeyes  8.125e-01  4.021e-01   2.020 0.043352 *  
DiabetesStatusDiabetes    -2.452e-01  3.580e-01  -0.685 0.493320    
SmokerCurrentyes           1.535e-02  3.156e-01   0.049 0.961216    
Med.Statin.LLDyes         -2.041e-01  3.500e-01  -0.583 0.559900    
Med.all.antiplateletyes    4.440e-02  5.323e-01   0.083 0.933531    
GFR_MDRD                   1.616e-03  8.527e-03   0.190 0.849698    
BMI                        5.189e-02  3.986e-02   1.302 0.192964    
CAD_history               -2.668e-01  3.395e-01  -0.786 0.431944    
Stroke_history             6.524e-01  3.304e-01   1.974 0.048329 *  
Peripheral.interv          1.371e-01  3.784e-01   0.362 0.717064    
stenose50-70%             -1.464e+01  9.783e+02  -0.015 0.988061    
stenose70-90%             -1.308e+01  9.783e+02  -0.013 0.989335    
stenose90-99%             -1.351e+01  9.783e+02  -0.014 0.988977    
stenose100% (Occlusion)   -1.354e+01  9.783e+02  -0.014 0.988957    
IL6_pg_ug_2015_LN          1.574e-01  1.003e-01   1.570 0.116511    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 367.47  on 390  degrees of freedom
Residual deviance: 318.31  on 372  degrees of freedom
AIC: 356.31

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.623593 
Standard error............: 0.18047 
Odds ratio (effect size)..: 1.866 
Lower 95% CI..............: 1.31 
Upper 95% CI..............: 2.657 
Z-value...................: 3.455391 
P-value...................: 0.0005494961 
Hosmer and Lemeshow r^2...: 0.133787 
Cox and Snell r^2.........: 0.118153 
Nagelkerke's pseudo r^2...: 0.193914 
Sample size of AE DB......: 2388 
Sample size of model......: 391 
Missing data %............: 83.62647 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    BMI, family = binomial(link = "logit"), data = currentDF)

Coefficients:
(Intercept)          Age   Gendermale          BMI  
   -2.47828      0.02961      0.56213      0.04578  

Degrees of Freedom: 390 Total (i.e. Null);  387 Residual
Null Deviance:      442.5 
Residual Deviance: 430.1    AIC: 438.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0691  -0.8812   0.6388   0.7834   1.5392  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                10.956858 617.026269   0.018   0.9858  
currentDF[, PROTEIN]        0.058760   0.152206   0.386   0.6995  
Age                         0.020541   0.015307   1.342   0.1796  
Gendermale                  0.587393   0.261581   2.246   0.0247 *
Hypertension.compositeyes   0.413722   0.344099   1.202   0.2292  
DiabetesStatusDiabetes     -0.444173   0.299202  -1.485   0.1377  
SmokerCurrentyes           -0.071966   0.266152  -0.270   0.7869  
Med.Statin.LLDyes          -0.080150   0.284447  -0.282   0.7781  
Med.all.antiplateletyes    -0.115336   0.453960  -0.254   0.7994  
GFR_MDRD                   -0.006601   0.007114  -0.928   0.3535  
BMI                         0.047927   0.033475   1.432   0.1522  
CAD_history                 0.123168   0.294174   0.419   0.6754  
Stroke_history              0.240489   0.261867   0.918   0.3584  
Peripheral.interv           0.364679   0.343814   1.061   0.2888  
stenose50-70%             -12.703643 617.023149  -0.021   0.9836  
stenose70-90%             -12.917664 617.022854  -0.021   0.9833  
stenose90-99%             -12.667837 617.022837  -0.021   0.9836  
stenose100% (Occlusion)   -12.703899 617.024124  -0.021   0.9836  
IL6_pg_ug_2015_LN           0.054674   0.082702   0.661   0.5086  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 442.47  on 390  degrees of freedom
Residual deviance: 419.44  on 372  degrees of freedom
AIC: 457.44

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: IPH 
Effect size...............: 0.05876 
Standard error............: 0.152206 
Odds ratio (effect size)..: 1.061 
Lower 95% CI..............: 0.787 
Upper 95% CI..............: 1.429 
Z-value...................: 0.386059 
P-value...................: 0.699453 
Hosmer and Lemeshow r^2...: 0.052051 
Cox and Snell r^2.........: 0.057201 
Nagelkerke's pseudo r^2...: 0.084431 
Sample size of AE DB......: 2388 
Sample size of model......: 391 
Missing data %............: 83.62647 

Analysis of IL6_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age         SmokerCurrentyes              CAD_history  
               -1.44653                 -0.07084                  0.01994                  0.39936                  0.25473  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%  
               -1.00356                 -0.50547                 -0.26062                  0.82473                -13.93339  
          stenose70-99%  
               -1.52574  

Degrees of Freedom: 996 Total (i.e. Null);  986 Residual
Null Deviance:      1381 
Residual Deviance: 1349     AIC: 1371

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6142  -1.1333  -0.7968   1.1560   1.6718  

Coefficients: (1 not defined because of singularities)
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                -1.129798   1.325225  -0.853  0.39392   
currentDF[, PROTEIN]       -0.069823   0.045164  -1.546  0.12211   
Age                         0.016974   0.008100   2.095  0.03613 * 
Gendermale                 -0.133605   0.144147  -0.927  0.35400   
Hypertension.compositeyes   0.232025   0.202513   1.146  0.25191   
DiabetesStatusDiabetes     -0.174736   0.159528  -1.095  0.27337   
SmokerCurrentyes            0.414304   0.146715   2.824  0.00474 **
Med.Statin.LLDyes          -0.164594   0.159158  -1.034  0.30106   
Med.all.antiplateletyes    -0.223848   0.217190  -1.031  0.30270   
GFR_MDRD                   -0.001882   0.003492  -0.539  0.58999   
BMI                         0.012407   0.018096   0.686  0.49295   
CAD_history                 0.263345   0.149976   1.756  0.07910 . 
Stroke_history             -0.138179   0.140751  -0.982  0.32623   
Peripheral.interv          -0.183211   0.172892  -1.060  0.28929   
stenose50-70%              -0.937991   0.962569  -0.974  0.32983   
stenose70-90%              -0.484572   0.928921  -0.522  0.60191   
stenose90-99%              -0.235047   0.928443  -0.253  0.80014   
stenose100% (Occlusion)     0.820707   1.245138   0.659  0.50981   
stenose50-99%             -14.005575 368.450051  -0.038  0.96968   
stenose70-99%              -1.450206   1.253664  -1.157  0.24736   
IL6_pg_ug_2015_LN                 NA         NA      NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1381.3  on 996  degrees of freedom
Residual deviance: 1340.1  on 977  degrees of freedom
AIC: 1380.1

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.069823 
Standard error............: 0.045164 
Odds ratio (effect size)..: 0.933 
Lower 95% CI..............: 0.854 
Upper 95% CI..............: 1.019 
Z-value...................: -1.545965 
P-value...................: 0.122113 
Hosmer and Lemeshow r^2...: 0.029786 
Cox and Snell r^2.........: 0.040427 
Nagelkerke's pseudo r^2...: 0.053918 
Sample size of AE DB......: 2388 
Sample size of model......: 997 
Missing data %............: 58.24958 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Hypertension.composite + SmokerCurrent + BMI + Stroke_history, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]  Hypertension.compositeyes           SmokerCurrentyes                        BMI  
                 -0.64730                   -0.19549                    0.33966                    0.44766                    0.03332  
           Stroke_history  
                  0.25010  

Degrees of Freedom: 999 Total (i.e. Null);  994 Residual
Null Deviance:      1017 
Residual Deviance: 993.2    AIC: 1005

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2738   0.4496   0.6096   0.7198   1.1160  

Coefficients: (1 not defined because of singularities)
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.232e+01  6.478e+02   0.019 0.984826    
currentDF[, PROTEIN]      -1.901e-01  5.643e-02  -3.370 0.000753 ***
Age                        8.224e-03  9.876e-03   0.833 0.405022    
Gendermale                -8.624e-02  1.781e-01  -0.484 0.628235    
Hypertension.compositeyes  3.110e-01  2.330e-01   1.335 0.181998    
DiabetesStatusDiabetes     2.110e-01  2.038e-01   1.035 0.300511    
SmokerCurrentyes           4.928e-01  1.863e-01   2.645 0.008165 ** 
Med.Statin.LLDyes         -1.679e-02  1.955e-01  -0.086 0.931556    
Med.all.antiplateletyes    1.321e-01  2.616e-01   0.505 0.613723    
GFR_MDRD                   4.699e-03  4.301e-03   1.092 0.274655    
BMI                        3.343e-02  2.363e-02   1.415 0.157050    
CAD_history                2.203e-01  1.882e-01   1.171 0.241696    
Stroke_history             2.401e-01  1.765e-01   1.360 0.173700    
Peripheral.interv         -1.730e-02  2.154e-01  -0.080 0.935980    
stenose50-70%             -1.361e+01  6.478e+02  -0.021 0.983245    
stenose70-90%             -1.401e+01  6.478e+02  -0.022 0.982741    
stenose90-99%             -1.406e+01  6.478e+02  -0.022 0.982686    
stenose100% (Occlusion)    5.521e-01  8.166e+02   0.001 0.999461    
stenose50-99%              1.725e-02  1.208e+03   0.000 0.999989    
stenose70-99%             -1.372e+01  6.478e+02  -0.021 0.983109    
IL6_pg_ug_2015_LN                 NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1017.22  on 999  degrees of freedom
Residual deviance:  980.22  on 980  degrees of freedom
AIC: 1020.2

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.190149 
Standard error............: 0.056431 
Odds ratio (effect size)..: 0.827 
Lower 95% CI..............: 0.74 
Upper 95% CI..............: 0.924 
Z-value...................: -3.369571 
P-value...................: 0.000752854 
Hosmer and Lemeshow r^2...: 0.03637 
Cox and Snell r^2.........: 0.036321 
Nagelkerke's pseudo r^2...: 0.056893 
Sample size of AE DB......: 2388 
Sample size of model......: 1000 
Missing data %............: 58.12395 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale        Stroke_history     Peripheral.interv  
              1.5088                0.3120                0.8361                0.3705               -0.6128  

Degrees of Freedom: 999 Total (i.e. Null);  995 Residual
Null Deviance:      1165 
Residual Deviance: 1078     AIC: 1088

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2376  -1.0048   0.6046   0.8035   2.0522  

Coefficients: (1 not defined because of singularities)
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.416e+01  3.908e+02   0.036 0.971101    
currentDF[, PROTEIN]       3.150e-01  5.445e-02   5.785 7.25e-09 ***
Age                        9.863e-03  9.331e-03   1.057 0.290516    
Gendermale                 8.540e-01  1.612e-01   5.299 1.16e-07 ***
Hypertension.compositeyes  6.925e-02  2.349e-01   0.295 0.768118    
DiabetesStatusDiabetes    -1.188e-01  1.840e-01  -0.646 0.518436    
SmokerCurrentyes           9.721e-02  1.711e-01   0.568 0.569863    
Med.Statin.LLDyes         -1.762e-01  1.905e-01  -0.925 0.354945    
Med.all.antiplateletyes    6.857e-02  2.500e-01   0.274 0.783889    
GFR_MDRD                  -2.444e-04  4.099e-03  -0.060 0.952452    
BMI                        4.105e-03  2.044e-02   0.201 0.840876    
CAD_history                8.625e-02  1.753e-01   0.492 0.622789    
Stroke_history             3.758e-01  1.703e-01   2.207 0.027313 *  
Peripheral.interv         -6.179e-01  1.860e-01  -3.322 0.000895 ***
stenose50-70%             -1.354e+01  3.908e+02  -0.035 0.972374    
stenose70-90%             -1.348e+01  3.908e+02  -0.035 0.972476    
stenose90-99%             -1.333e+01  3.908e+02  -0.034 0.972791    
stenose100% (Occlusion)   -1.432e+01  3.908e+02  -0.037 0.970764    
stenose50-99%             -1.499e+01  3.908e+02  -0.038 0.969397    
stenose70-99%             -1.463e+01  3.908e+02  -0.037 0.970143    
IL6_pg_ug_2015_LN                 NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1164.5  on 999  degrees of freedom
Residual deviance: 1066.6  on 980  degrees of freedom
AIC: 1106.6

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.314972 
Standard error............: 0.054447 
Odds ratio (effect size)..: 1.37 
Lower 95% CI..............: 1.232 
Upper 95% CI..............: 1.525 
Z-value...................: 5.78494 
P-value...................: 7.25384e-09 
Hosmer and Lemeshow r^2...: 0.084067 
Cox and Snell r^2.........: 0.093259 
Nagelkerke's pseudo r^2...: 0.135564 
Sample size of AE DB......: 2388 
Sample size of model......: 1000 
Missing data %............: 58.12395 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Med.Statin.LLD + 
    BMI + CAD_history + Stroke_history + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)               Gendermale        Med.Statin.LLDyes                      BMI              CAD_history  
                0.16028                  0.59271                 -0.25963                  0.02923                  0.30318  
         Stroke_history            stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
                0.25028                 -1.09743                 -0.97086                 -0.65159                 -0.80087  
          stenose50-99%            stenose70-99%  
              -15.27559                  0.59126  

Degrees of Freedom: 998 Total (i.e. Null);  987 Residual
Null Deviance:      1331 
Residual Deviance: 1288     AIC: 1312

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9361  -1.2697   0.8098   0.9793   1.4501  

Coefficients: (1 not defined because of singularities)
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 0.257533   1.493884   0.172   0.8631    
currentDF[, PROTEIN]        0.039778   0.046572   0.854   0.3930    
Age                         0.001667   0.008309   0.201   0.8410    
Gendermale                  0.639327   0.146514   4.364 1.28e-05 ***
Hypertension.compositeyes  -0.107679   0.207227  -0.520   0.6033    
DiabetesStatusDiabetes     -0.123508   0.163543  -0.755   0.4501    
SmokerCurrentyes            0.124348   0.151574   0.820   0.4120    
Med.Statin.LLDyes          -0.249362   0.167276  -1.491   0.1360    
Med.all.antiplateletyes     0.162196   0.221065   0.734   0.4631    
GFR_MDRD                   -0.004962   0.003614  -1.373   0.1698    
BMI                         0.033097   0.018698   1.770   0.0767 .  
CAD_history                 0.311877   0.157241   1.983   0.0473 *  
Stroke_history              0.230077   0.146495   1.571   0.1163    
Peripheral.interv           0.037229   0.178257   0.209   0.8346    
stenose50-70%              -1.012619   1.165333  -0.869   0.3849    
stenose70-90%              -0.905980   1.139839  -0.795   0.4267    
stenose90-99%              -0.591956   1.139857  -0.519   0.6035    
stenose100% (Occlusion)    -0.746506   1.357852  -0.550   0.5825    
stenose50-99%             -15.241325 376.943340  -0.040   0.9677    
stenose70-99%               0.596010   1.581074   0.377   0.7062    
IL6_pg_ug_2015_LN                 NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1331.0  on 998  degrees of freedom
Residual deviance: 1283.3  on 979  degrees of freedom
AIC: 1323.3

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: 0.039778 
Standard error............: 0.046572 
Odds ratio (effect size)..: 1.041 
Lower 95% CI..............: 0.95 
Upper 95% CI..............: 1.14 
Z-value...................: 0.85412 
P-value...................: 0.3930386 
Hosmer and Lemeshow r^2...: 0.035859 
Cox and Snell r^2.........: 0.046653 
Nagelkerke's pseudo r^2...: 0.063374 
Sample size of AE DB......: 2388 
Sample size of model......: 999 
Missing data %............: 58.16583 

Analysis of IL6R_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent + 
    CAD_history + stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)                      Age         SmokerCurrentyes              CAD_history            stenose50-70%  
                -0.5484                   0.0167                   0.3493                   0.2402                  -1.6362  
          stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
                -1.1930                  -0.9512                   0.1470                 -14.6281                  -2.1898  
      IL6_pg_ug_2015_LN  
                -0.0803  

Degrees of Freedom: 963 Total (i.e. Null);  953 Residual
Null Deviance:      1336 
Residual Deviance: 1307     AIC: 1329

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6363  -1.1288  -0.8086   1.1628   1.6918  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                 0.131467   1.509873   0.087   0.9306  
currentDF[, PROTEIN]        0.015880   0.064650   0.246   0.8060  
Age                         0.012327   0.008316   1.482   0.1383  
Gendermale                 -0.137060   0.147383  -0.930   0.3524  
Hypertension.compositeyes   0.234963   0.204339   1.150   0.2502  
DiabetesStatusDiabetes     -0.225859   0.162459  -1.390   0.1645  
SmokerCurrentyes            0.351674   0.149268   2.356   0.0185 *
Med.Statin.LLDyes          -0.160052   0.163105  -0.981   0.3265  
Med.all.antiplateletyes    -0.279489   0.221207  -1.263   0.2064  
GFR_MDRD                   -0.002737   0.003625  -0.755   0.4502  
BMI                         0.005206   0.018794   0.277   0.7818  
CAD_history                 0.256263   0.152852   1.677   0.0936 .
Stroke_history             -0.127472   0.142976  -0.892   0.3726  
Peripheral.interv          -0.198088   0.177734  -1.115   0.2651  
stenose50-70%              -1.500411   1.195569  -1.255   0.2095  
stenose70-90%              -1.121396   1.168598  -0.960   0.3373  
stenose90-99%              -0.870897   1.168781  -0.745   0.4562  
stenose100% (Occlusion)     0.178054   1.432143   0.124   0.9011  
stenose50-99%             -14.651114 369.668141  -0.040   0.9684  
stenose70-99%              -2.052663   1.439555  -1.426   0.1539  
IL6_pg_ug_2015_LN          -0.086194   0.049532  -1.740   0.0818 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1335.6  on 963  degrees of freedom
Residual deviance: 1297.1  on 943  degrees of freedom
AIC: 1339.1

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.01588 
Standard error............: 0.06465 
Odds ratio (effect size)..: 1.016 
Lower 95% CI..............: 0.895 
Upper 95% CI..............: 1.153 
Z-value...................: 0.245629 
P-value...................: 0.8059697 
Hosmer and Lemeshow r^2...: 0.028821 
Cox and Snell r^2.........: 0.039144 
Nagelkerke's pseudo r^2...: 0.052206 
Sample size of AE DB......: 2388 
Sample size of model......: 964 
Missing data %............: 59.63149 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    SmokerCurrent + CAD_history + Stroke_history + IL6_pg_ug_2015_LN, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes           SmokerCurrentyes                CAD_history             Stroke_history  
                   0.1065                     0.3455                     0.4323                     0.2732                     0.2837  
        IL6_pg_ug_2015_LN  
                  -0.2068  

Degrees of Freedom: 966 Total (i.e. Null);  961 Residual
Null Deviance:      985.8 
Residual Deviance: 960.7    AIC: 972.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2145   0.4440   0.6086   0.7177   1.1783  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.259e+01  7.227e+02   0.017 0.986097    
currentDF[, PROTEIN]       5.215e-02  8.232e-02   0.634 0.526381    
Age                        7.063e-03  1.015e-02   0.696 0.486455    
Gendermale                -3.182e-02  1.811e-01  -0.176 0.860579    
Hypertension.compositeyes  3.185e-01  2.345e-01   1.358 0.174433    
DiabetesStatusDiabetes     2.481e-01  2.084e-01   1.190 0.234003    
SmokerCurrentyes           4.669e-01  1.891e-01   2.469 0.013536 *  
Med.Statin.LLDyes         -4.153e-03  2.003e-01  -0.021 0.983458    
Med.all.antiplateletyes    1.838e-01  2.645e-01   0.695 0.487095    
GFR_MDRD                   3.662e-03  4.444e-03   0.824 0.409912    
BMI                        2.659e-02  2.439e-02   1.090 0.275597    
CAD_history                2.855e-01  1.932e-01   1.477 0.139611    
Stroke_history             2.739e-01  1.794e-01   1.527 0.126881    
Peripheral.interv         -6.486e-02  2.204e-01  -0.294 0.768565    
stenose50-70%             -1.368e+01  7.227e+02  -0.019 0.984895    
stenose70-90%             -1.407e+01  7.227e+02  -0.019 0.984466    
stenose90-99%             -1.409e+01  7.227e+02  -0.019 0.984450    
stenose100% (Occlusion)    5.863e-01  8.745e+02   0.001 0.999465    
stenose50-99%             -6.587e-02  1.250e+03   0.000 0.999958    
stenose70-99%             -1.373e+01  7.227e+02  -0.019 0.984847    
IL6_pg_ug_2015_LN         -2.201e-01  6.190e-02  -3.555 0.000378 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 985.80  on 966  degrees of freedom
Residual deviance: 948.12  on 946  degrees of freedom
AIC: 990.12

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.052151 
Standard error............: 0.082317 
Odds ratio (effect size)..: 1.054 
Lower 95% CI..............: 0.897 
Upper 95% CI..............: 1.238 
Z-value...................: 0.633541 
P-value...................: 0.5263807 
Hosmer and Lemeshow r^2...: 0.038216 
Cox and Snell r^2.........: 0.03821 
Nagelkerke's pseudo r^2...: 0.059778 
Sample size of AE DB......: 2388 
Sample size of model......: 967 
Missing data %............: 59.50586 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Stroke_history + 
    Peripheral.interv + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
      (Intercept)         Gendermale     Stroke_history  Peripheral.interv  IL6_pg_ug_2015_LN  
           1.5720             0.7867             0.3729            -0.6177             0.3207  

Degrees of Freedom: 966 Total (i.e. Null);  962 Residual
Null Deviance:      1120 
Residual Deviance: 1038     AIC: 1048

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2342  -1.0138   0.6048   0.7899   1.9678  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.395e+01  4.391e+02   0.032  0.97466    
currentDF[, PROTEIN]      -7.853e-02  7.347e-02  -1.069  0.28509    
Age                        1.118e-02  9.683e-03   1.154  0.24832    
Gendermale                 7.989e-01  1.656e-01   4.825  1.4e-06 ***
Hypertension.compositeyes  5.443e-02  2.395e-01   0.227  0.82023    
DiabetesStatusDiabetes    -7.246e-02  1.884e-01  -0.385  0.70045    
SmokerCurrentyes           1.276e-01  1.752e-01   0.728  0.46643    
Med.Statin.LLDyes         -1.814e-01  1.955e-01  -0.928  0.35336    
Med.all.antiplateletyes    7.743e-02  2.552e-01   0.303  0.76159    
GFR_MDRD                   5.217e-04  4.295e-03   0.121  0.90331    
BMI                       -8.952e-04  2.143e-02  -0.042  0.96668    
CAD_history                6.938e-02  1.792e-01   0.387  0.69860    
Stroke_history             3.818e-01  1.736e-01   2.199  0.02785 *  
Peripheral.interv         -5.960e-01  1.914e-01  -3.114  0.00185 ** 
stenose50-70%             -1.335e+01  4.391e+02  -0.030  0.97574    
stenose70-90%             -1.332e+01  4.391e+02  -0.030  0.97580    
stenose90-99%             -1.315e+01  4.391e+02  -0.030  0.97610    
stenose100% (Occlusion)   -1.417e+01  4.391e+02  -0.032  0.97425    
stenose50-99%             -1.479e+01  4.391e+02  -0.034  0.97314    
stenose70-99%             -1.456e+01  4.391e+02  -0.033  0.97355    
IL6_pg_ug_2015_LN          3.472e-01  6.028e-02   5.760  8.4e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1119.8  on 966  degrees of freedom
Residual deviance: 1026.6  on 946  degrees of freedom
AIC: 1068.6

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.078534 
Standard error............: 0.073468 
Odds ratio (effect size)..: 0.924 
Lower 95% CI..............: 0.8 
Upper 95% CI..............: 1.068 
Z-value...................: -1.068964 
P-value...................: 0.285086 
Hosmer and Lemeshow r^2...: 0.083245 
Cox and Snell r^2.........: 0.091898 
Nagelkerke's pseudo r^2...: 0.133984 
Sample size of AE DB......: 2388 
Sample size of model......: 967 
Missing data %............: 59.50586 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + GFR_MDRD + CAD_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale              GFR_MDRD           CAD_history  
            0.579608              0.099350              0.609467             -0.005617              0.258415  

Degrees of Freedom: 965 Total (i.e. Null);  961 Residual
Null Deviance:      1287 
Residual Deviance: 1260     AIC: 1270

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9401  -1.2758   0.8087   0.9790   1.4683  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 0.870087   1.542261   0.564   0.5726    
currentDF[, PROTEIN]        0.075391   0.066105   1.140   0.2541    
Age                        -0.002322   0.008571  -0.271   0.7864    
Gendermale                  0.652003   0.149841   4.351 1.35e-05 ***
Hypertension.compositeyes  -0.110273   0.209219  -0.527   0.5981    
DiabetesStatusDiabetes     -0.162545   0.166002  -0.979   0.3275    
SmokerCurrentyes            0.105910   0.154553   0.685   0.4932    
Med.Statin.LLDyes          -0.213342   0.171102  -1.247   0.2124    
Med.all.antiplateletyes     0.100133   0.225696   0.444   0.6573    
GFR_MDRD                   -0.006057   0.003763  -1.610   0.1075    
BMI                         0.021591   0.019375   1.114   0.2651    
CAD_history                 0.336940   0.160619   2.098   0.0359 *  
Stroke_history              0.160102   0.148241   1.080   0.2801    
Peripheral.interv           0.011627   0.183433   0.063   0.9495    
stenose50-70%              -0.707300   1.213509  -0.583   0.5600    
stenose70-90%              -0.719550   1.188270  -0.606   0.5448    
stenose90-99%              -0.430171   1.188886  -0.362   0.7175    
stenose100% (Occlusion)    -0.569412   1.397842  -0.407   0.6838    
stenose50-99%             -15.053372 375.980709  -0.040   0.9681    
stenose70-99%               0.882110   1.614884   0.546   0.5849    
IL6_pg_ug_2015_LN           0.019723   0.051089   0.386   0.6995    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1286.7  on 965  degrees of freedom
Residual deviance: 1241.9  on 945  degrees of freedom
AIC: 1283.9

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: 0.075391 
Standard error............: 0.066105 
Odds ratio (effect size)..: 1.078 
Lower 95% CI..............: 0.947 
Upper 95% CI..............: 1.227 
Z-value...................: 1.140483 
P-value...................: 0.2540851 
Hosmer and Lemeshow r^2...: 0.034884 
Cox and Snell r^2.........: 0.045403 
Nagelkerke's pseudo r^2...: 0.061684 
Sample size of AE DB......: 2388 
Sample size of model......: 966 
Missing data %............: 59.54774 

Analysis of MCP1_pg_ug_2015_LN.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + SmokerCurrent + Med.Statin.LLD + CAD_history + IL6_pg_ug_2015_LN, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age      SmokerCurrentyes     Med.Statin.LLDyes           CAD_history  
            -1.68186              -0.46232               0.02094               0.43073              -0.24592               0.28851  
   IL6_pg_ug_2015_LN  
             0.12975  

Degrees of Freedom: 995 Total (i.e. Null);  989 Residual
Null Deviance:      1380 
Residual Deviance: 1300     AIC: 1314

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0805  -1.0624  -0.6758   1.0971   2.0048  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                -1.247022   1.352245  -0.922   0.3564    
currentDF[, PROTEIN]       -0.443863   0.061641  -7.201 5.99e-13 ***
Age                         0.019515   0.008385   2.327   0.0199 *  
Gendermale                 -0.114207   0.148426  -0.769   0.4416    
Hypertension.compositeyes   0.175828   0.208273   0.844   0.3985    
DiabetesStatusDiabetes     -0.203647   0.164527  -1.238   0.2158    
SmokerCurrentyes            0.424195   0.151463   2.801   0.0051 ** 
Med.Statin.LLDyes          -0.237246   0.164315  -1.444   0.1488    
Med.all.antiplateletyes    -0.250764   0.222840  -1.125   0.2605    
GFR_MDRD                   -0.001521   0.003610  -0.421   0.6735    
BMI                         0.011814   0.018663   0.633   0.5267    
CAD_history                 0.291490   0.154775   1.883   0.0597 .  
Stroke_history             -0.136104   0.144685  -0.941   0.3469    
Peripheral.interv          -0.129558   0.177907  -0.728   0.4665    
stenose50-70%              -0.808165   0.970477  -0.833   0.4050    
stenose70-90%              -0.351555   0.935233  -0.376   0.7070    
stenose90-99%              -0.169215   0.934686  -0.181   0.8563    
stenose100% (Occlusion)     0.538300   1.257511   0.428   0.6686    
stenose50-99%             -13.583568 359.187307  -0.038   0.9698    
stenose70-99%              -1.070720   1.269341  -0.844   0.3989    
IL6_pg_ug_2015_LN           0.116989   0.053110   2.203   0.0276 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1379.8  on 995  degrees of freedom
Residual deviance: 1283.1  on 975  degrees of freedom
AIC: 1325.1

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.443863 
Standard error............: 0.061641 
Odds ratio (effect size)..: 0.642 
Lower 95% CI..............: 0.569 
Upper 95% CI..............: 0.724 
Z-value...................: -7.200765 
P-value...................: 5.987587e-13 
Hosmer and Lemeshow r^2...: 0.070094 
Cox and Snell r^2.........: 0.092541 
Nagelkerke's pseudo r^2...: 0.123426 
Sample size of AE DB......: 2388 
Sample size of model......: 996 
Missing data %............: 58.29146 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerCurrent + BMI + Stroke_history + IL6_pg_ug_2015_LN, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]      SmokerCurrentyes                   BMI        Stroke_history     IL6_pg_ug_2015_LN  
            -0.42580              -0.14037               0.41148               0.03668               0.25014              -0.14422  

Degrees of Freedom: 998 Total (i.e. Null);  993 Residual
Null Deviance:      1017 
Residual Deviance: 991.3    AIC: 1003

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4197   0.4376   0.6130   0.7213   1.1331  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.226e+01  6.464e+02   0.019  0.98487   
currentDF[, PROTEIN]      -1.355e-01  7.212e-02  -1.879  0.06027 . 
Age                        8.672e-03  9.911e-03   0.875  0.38160   
Gendermale                -8.085e-02  1.786e-01  -0.453  0.65073   
Hypertension.compositeyes  2.949e-01  2.331e-01   1.265  0.20584   
DiabetesStatusDiabetes     2.090e-01  2.043e-01   1.023  0.30638   
SmokerCurrentyes           4.927e-01  1.869e-01   2.637  0.00837 **
Med.Statin.LLDyes         -3.188e-02  1.964e-01  -0.162  0.87101   
Med.all.antiplateletyes    1.278e-01  2.616e-01   0.489  0.62514   
GFR_MDRD                   4.933e-03  4.319e-03   1.142  0.25337   
BMI                        3.417e-02  2.384e-02   1.433  0.15186   
CAD_history                2.175e-01  1.887e-01   1.153  0.24897   
Stroke_history             2.438e-01  1.767e-01   1.380  0.16769   
Peripheral.interv         -1.828e-03  2.160e-01  -0.008  0.99325   
stenose50-70%             -1.355e+01  6.464e+02  -0.021  0.98327   
stenose70-90%             -1.397e+01  6.464e+02  -0.022  0.98276   
stenose90-99%             -1.403e+01  6.464e+02  -0.022  0.98268   
stenose100% (Occlusion)    4.510e-01  8.168e+02   0.001  0.99956   
stenose50-99%              1.631e-01  1.205e+03   0.000  0.99989   
stenose70-99%             -1.358e+01  6.464e+02  -0.021  0.98324   
IL6_pg_ug_2015_LN         -1.360e-01  6.296e-02  -2.161  0.03070 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1016.8  on 998  degrees of freedom
Residual deviance:  976.4  on 978  degrees of freedom
AIC: 1018.4

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.135492 
Standard error............: 0.072117 
Odds ratio (effect size)..: 0.873 
Lower 95% CI..............: 0.758 
Upper 95% CI..............: 1.006 
Z-value...................: -1.878788 
P-value...................: 0.06027341 
Hosmer and Lemeshow r^2...: 0.039691 
Cox and Snell r^2.........: 0.039592 
Nagelkerke's pseudo r^2...: 0.061998 
Sample size of AE DB......: 2388 
Sample size of model......: 999 
Missing data %............: 58.16583 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Stroke_history + 
    Peripheral.interv + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
      (Intercept)         Gendermale     Stroke_history  Peripheral.interv  IL6_pg_ug_2015_LN  
           1.5273             0.8331             0.3735            -0.6084             0.3181  

Degrees of Freedom: 998 Total (i.e. Null);  994 Residual
Null Deviance:      1164 
Residual Deviance: 1076     AIC: 1086

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2589  -1.0101   0.6018   0.7920   2.0993  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.415e+01  3.921e+02   0.036  0.97120    
currentDF[, PROTEIN]      -7.151e-02  6.520e-02  -1.097  0.27273    
Age                        1.046e-02  9.349e-03   1.119  0.26300    
Gendermale                 8.575e-01  1.615e-01   5.310 1.10e-07 ***
Hypertension.compositeyes  5.600e-02  2.356e-01   0.238  0.81211    
DiabetesStatusDiabetes    -1.209e-01  1.842e-01  -0.657  0.51145    
SmokerCurrentyes           9.488e-02  1.714e-01   0.554  0.57983    
Med.Statin.LLDyes         -1.720e-01  1.913e-01  -0.899  0.36850    
Med.all.antiplateletyes    5.990e-02  2.504e-01   0.239  0.81092    
GFR_MDRD                  -1.064e-04  4.107e-03  -0.026  0.97934    
BMI                        3.479e-03  2.047e-02   0.170  0.86504    
CAD_history                7.993e-02  1.756e-01   0.455  0.64903    
Stroke_history             3.777e-01  1.703e-01   2.218  0.02657 *  
Peripheral.interv         -6.043e-01  1.864e-01  -3.242  0.00119 ** 
stenose50-70%             -1.350e+01  3.921e+02  -0.034  0.97253    
stenose70-90%             -1.346e+01  3.921e+02  -0.034  0.97262    
stenose90-99%             -1.332e+01  3.921e+02  -0.034  0.97290    
stenose100% (Occlusion)   -1.437e+01  3.921e+02  -0.037  0.97078    
stenose50-99%             -1.491e+01  3.921e+02  -0.038  0.96967    
stenose70-99%             -1.456e+01  3.921e+02  -0.037  0.97039    
IL6_pg_ug_2015_LN          3.528e-01  6.258e-02   5.637 1.73e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1163.9  on 998  degrees of freedom
Residual deviance: 1064.2  on 978  degrees of freedom
AIC: 1106.2

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.071508 
Standard error............: 0.065197 
Odds ratio (effect size)..: 0.931 
Lower 95% CI..............: 0.819 
Upper 95% CI..............: 1.058 
Z-value...................: -1.096804 
P-value...................: 0.2727272 
Hosmer and Lemeshow r^2...: 0.085641 
Cox and Snell r^2.........: 0.094961 
Nagelkerke's pseudo r^2...: 0.138006 
Sample size of AE DB......: 2388 
Sample size of model......: 999 
Missing data %............: 58.16583 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Med.Statin.LLD + BMI + CAD_history + Stroke_history + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]               Gendermale        Med.Statin.LLDyes                      BMI  
                0.29150                 -0.16175                  0.61178                 -0.30170                  0.02949  
            CAD_history           Stroke_history            stenose50-70%            stenose70-90%            stenose90-99%  
                0.33164                  0.24139                 -1.04294                 -0.93615                 -0.63010  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%        IL6_pg_ug_2015_LN  
               -0.94859                -15.14790                  0.78602                  0.10540  

Degrees of Freedom: 997 Total (i.e. Null);  984 Residual
Null Deviance:      1329 
Residual Deviance: 1277     AIC: 1305

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9763  -1.2540   0.7873   0.9730   1.5792  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 0.329613   1.502995   0.219  0.82641    
currentDF[, PROTEIN]       -0.164322   0.059211  -2.775  0.00552 ** 
Age                         0.001184   0.008365   0.142  0.88744    
Gendermale                  0.661474   0.147568   4.483 7.38e-06 ***
Hypertension.compositeyes  -0.132227   0.208361  -0.635  0.52569    
DiabetesStatusDiabetes     -0.140503   0.164344  -0.855  0.39259    
SmokerCurrentyes            0.110164   0.152548   0.722  0.47020    
Med.Statin.LLDyes          -0.296021   0.168954  -1.752  0.07976 .  
Med.all.antiplateletyes     0.162553   0.222186   0.732  0.46441    
GFR_MDRD                   -0.005252   0.003634  -1.445  0.14847    
BMI                         0.033085   0.018873   1.753  0.07959 .  
CAD_history                 0.331367   0.158390   2.092  0.03643 *  
Stroke_history              0.235023   0.147134   1.597  0.11019    
Peripheral.interv           0.050946   0.179307   0.284  0.77632    
stenose50-70%              -0.969413   1.172996  -0.826  0.40855    
stenose70-90%              -0.862961   1.147375  -0.752  0.45198    
stenose90-99%              -0.564964   1.147362  -0.492  0.62243    
stenose100% (Occlusion)    -0.861362   1.365522  -0.631  0.52818    
stenose50-99%             -15.111635 375.012524  -0.040  0.96786    
stenose70-99%               0.760363   1.594802   0.477  0.63352    
IL6_pg_ug_2015_LN           0.102235   0.053419   1.914  0.05564 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1329.1  on 997  degrees of freedom
Residual deviance: 1272.6  on 977  degrees of freedom
AIC: 1314.6

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: IPH 
Effect size...............: -0.164322 
Standard error............: 0.059211 
Odds ratio (effect size)..: 0.848 
Lower 95% CI..............: 0.756 
Upper 95% CI..............: 0.953 
Z-value...................: -2.775196 
P-value...................: 0.005516838 
Hosmer and Lemeshow r^2...: 0.04254 
Cox and Snell r^2.........: 0.055078 
Nagelkerke's pseudo r^2...: 0.074835 
Sample size of AE DB......: 2388 
Sample size of model......: 998 
Missing data %............: 58.2077 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
Running linear regression...
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5rank) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_rank.

- processing Macrophages_rank
Note: Using an external vector in selections is ambiguous.
ℹ Use `all_of(COVARIATES_M5rank)` instead of `COVARIATES_M5rank` to silence this message.
ℹ See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
This message is displayed once per session.

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD + 
    Med.all.antiplatelet + CAD_history + IL6_pg_ug_2015_rank, 
    data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]        Med.Statin.LLDyes  Med.all.antiplateletyes              CAD_history  
                0.35989                 -0.07726                 -0.21850                 -0.25457                  0.16345  
    IL6_pg_ug_2015_rank  
                0.16591  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.84744 -0.68617  0.08551  0.63084  2.76718 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                2.2031063  1.0305201   2.138 0.033217 *  
currentDF[, TRAIT]        -0.0742790  0.0467605  -1.589 0.113071    
Age                       -0.0066882  0.0063518  -1.053 0.293082    
Gendermale                -0.1099818  0.1127155  -0.976 0.329860    
Hypertension.compositeyes  0.0267508  0.1561093   0.171 0.864040    
DiabetesStatusDiabetes    -0.0280114  0.1276448  -0.219 0.826428    
SmokerCurrentyes           0.0254452  0.1100166   0.231 0.817228    
Med.Statin.LLDyes         -0.2522453  0.1137454  -2.218 0.027218 *  
Med.all.antiplateletyes   -0.3217332  0.1793115  -1.794 0.073628 .  
GFR_MDRD                  -0.0006063  0.0028743  -0.211 0.833055    
BMI                       -0.0125403  0.0141299  -0.888 0.375415    
CAD_history                0.1796808  0.1146154   1.568 0.117853    
Stroke_history             0.0582772  0.1067541   0.546 0.585480    
Peripheral.interv          0.0944270  0.1298333   0.727 0.467529    
stenose50-70%             -0.6811138  0.7287012  -0.935 0.350586    
stenose70-90%             -0.9512645  0.6846125  -1.389 0.165561    
stenose90-99%             -0.9277803  0.6818376  -1.361 0.174477    
stenose100% (Occlusion)   -1.3783689  0.8486138  -1.624 0.105216    
IL6_pg_ug_2015_rank        0.1649700  0.0488477   3.377 0.000814 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9506 on 352 degrees of freedom
Multiple R-squared:  0.08119,   Adjusted R-squared:  0.03421 
F-statistic: 1.728 on 18 and 352 DF,  p-value: 0.03306

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: -0.074279 
Standard error............: 0.04676 
Odds ratio (effect size)..: 0.928 
Lower 95% CI..............: 0.847 
Upper 95% CI..............: 1.018 
T-value...................: -1.588501 
P-value...................: 0.1130708 
R^2.......................: 0.081194 
Adjusted r^2..............: 0.03421 
Sample size of AE DB......: 2388 
Sample size of model......: 371 
Missing data %............: 84.46399 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD + 
    Med.all.antiplatelet + IL6_pg_ug_2015_rank, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]        Med.Statin.LLDyes  Med.all.antiplateletyes      IL6_pg_ug_2015_rank  
                0.39785                  0.08688                 -0.19540                 -0.26346                  0.17996  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.92927 -0.67421  0.02866  0.64141  2.58659 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                2.0867567  1.0364337   2.013 0.044840 *  
currentDF[, TRAIT]         0.0637693  0.0515246   1.238 0.216679    
Age                       -0.0051687  0.0065107  -0.794 0.427807    
Gendermale                -0.1036623  0.1149227  -0.902 0.367669    
Hypertension.compositeyes  0.0483875  0.1568018   0.309 0.757817    
DiabetesStatusDiabetes    -0.0160488  0.1278983  -0.125 0.900215    
SmokerCurrentyes           0.0332253  0.1100576   0.302 0.762916    
Med.Statin.LLDyes         -0.2497548  0.1153095  -2.166 0.030991 *  
Med.all.antiplateletyes   -0.3157180  0.1799548  -1.754 0.080235 .  
GFR_MDRD                  -0.0005197  0.0028850  -0.180 0.857160    
BMI                       -0.0126116  0.0141800  -0.889 0.374405    
CAD_history                0.1392717  0.1155741   1.205 0.229003    
Stroke_history             0.0502429  0.1072670   0.468 0.639797    
Peripheral.interv          0.0825096  0.1308781   0.630 0.528825    
stenose50-70%             -0.7117173  0.7319912  -0.972 0.331573    
stenose70-90%             -0.9703349  0.6867419  -1.413 0.158560    
stenose90-99%             -0.9317509  0.6838765  -1.362 0.173933    
stenose100% (Occlusion)   -1.3799752  0.8511497  -1.621 0.105855    
IL6_pg_ug_2015_rank        0.1781599  0.0498129   3.577 0.000397 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9533 on 349 degrees of freedom
Multiple R-squared:  0.07877,   Adjusted R-squared:  0.03126 
F-statistic: 1.658 on 18 and 349 DF,  p-value: 0.04502

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: 0.063769 
Standard error............: 0.051525 
Odds ratio (effect size)..: 1.066 
Lower 95% CI..............: 0.963 
Upper 95% CI..............: 1.179 
T-value...................: 1.237647 
P-value...................: 0.2166789 
R^2.......................: 0.078772 
Adjusted r^2..............: 0.031259 
Sample size of AE DB......: 2388 
Sample size of model......: 368 
Missing data %............: 84.58961 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD + Med.all.antiplatelet + 
    IL6_pg_ug_2015_rank, data = currentDF)

Coefficients:
            (Intercept)        Med.Statin.LLDyes  Med.all.antiplateletyes      IL6_pg_ug_2015_rank  
                 0.3946                  -0.1937                  -0.2655                   0.1654  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-1.90308 -0.67707  0.07394  0.63223  2.76744 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                2.150e+00  1.045e+00   2.058 0.040363 *  
currentDF[, TRAIT]        -6.949e-02  6.396e-02  -1.087 0.277974    
Age                       -6.905e-03  6.442e-03  -1.072 0.284480    
Gendermale                -1.054e-01  1.141e-01  -0.923 0.356568    
Hypertension.compositeyes  8.635e-02  1.604e-01   0.538 0.590637    
DiabetesStatusDiabetes    -1.333e-02  1.308e-01  -0.102 0.918894    
SmokerCurrentyes           4.174e-02  1.116e-01   0.374 0.708632    
Med.Statin.LLDyes         -2.492e-01  1.155e-01  -2.158 0.031634 *  
Med.all.antiplateletyes   -3.307e-01  1.808e-01  -1.829 0.068294 .  
GFR_MDRD                   1.827e-05  2.932e-03   0.006 0.995032    
BMI                       -1.267e-02  1.431e-02  -0.885 0.376633    
CAD_history                1.581e-01  1.160e-01   1.363 0.173663    
Stroke_history             9.170e-02  1.091e-01   0.840 0.401297    
Peripheral.interv          9.252e-02  1.327e-01   0.697 0.486219    
stenose50-70%             -6.830e-01  7.339e-01  -0.931 0.352696    
stenose70-90%             -9.892e-01  6.899e-01  -1.434 0.152541    
stenose90-99%             -9.555e-01  6.868e-01  -1.391 0.165052    
stenose100% (Occlusion)   -1.414e+00  8.559e-01  -1.652 0.099411 .  
IL6_pg_ug_2015_rank        1.670e-01  4.948e-02   3.374 0.000824 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9564 on 344 degrees of freedom
Multiple R-squared:  0.07774,   Adjusted R-squared:  0.02949 
F-statistic: 1.611 on 18 and 344 DF,  p-value: 0.0551

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.069495 
Standard error............: 0.063956 
Odds ratio (effect size)..: 0.933 
Lower 95% CI..............: 0.823 
Upper 95% CI..............: 1.057 
T-value...................: -1.086601 
P-value...................: 0.2779738 
R^2.......................: 0.077744 
Adjusted r^2..............: 0.029487 
Sample size of AE DB......: 2388 
Sample size of model......: 363 
Missing data %............: 84.799 

Analysis of MCP1_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + Med.Statin.LLD + 
    Med.all.antiplatelet + CAD_history + Peripheral.interv + 
    IL6_pg_ug_2015_rank, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale  Hypertension.compositeyes  
                 0.455323                   0.098950                  -0.007998                   0.239787                  -0.212282  
   DiabetesStatusDiabetes          Med.Statin.LLDyes    Med.all.antiplateletyes                CAD_history          Peripheral.interv  
                -0.274062                  -0.208990                   0.383777                   0.199395                  -0.177163  
      IL6_pg_ug_2015_rank  
                 0.281379  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.66159 -0.65202  0.01668  0.65824  2.64720 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.210790   0.981220   1.234   0.2180    
currentDF[, TRAIT]         0.093391   0.044733   2.088   0.0375 *  
Age                       -0.010588   0.006050  -1.750   0.0809 .  
Gendermale                 0.242507   0.105839   2.291   0.0225 *  
Hypertension.compositeyes -0.219585   0.144592  -1.519   0.1297    
DiabetesStatusDiabetes    -0.266244   0.120184  -2.215   0.0273 *  
SmokerCurrentyes          -0.047201   0.104990  -0.450   0.6533    
Med.Statin.LLDyes         -0.230203   0.110357  -2.086   0.0377 *  
Med.all.antiplateletyes    0.326921   0.167864   1.948   0.0522 .  
GFR_MDRD                  -0.002338   0.002739  -0.853   0.3939    
BMI                       -0.008989   0.013129  -0.685   0.4940    
CAD_history                0.195402   0.110685   1.765   0.0783 .  
Stroke_history             0.092587   0.101208   0.915   0.3609    
Peripheral.interv         -0.190373   0.127441  -1.494   0.1361    
stenose50-70%             -0.081469   0.715759  -0.114   0.9094    
stenose70-90%             -0.095273   0.672675  -0.142   0.8874    
stenose90-99%             -0.124982   0.669862  -0.187   0.8521    
stenose100% (Occlusion)   -0.947978   0.832048  -1.139   0.2553    
IL6_pg_ug_2015_rank        0.276120   0.045899   6.016 4.25e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9349 on 376 degrees of freedom
Multiple R-squared:  0.1664,    Adjusted R-squared:  0.1265 
F-statistic: 4.171 on 18 and 376 DF,  p-value: 4.738e-08

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.093391 
Standard error............: 0.044733 
Odds ratio (effect size)..: 1.098 
Lower 95% CI..............: 1.006 
Upper 95% CI..............: 1.198 
T-value...................: 2.087753 
P-value...................: 0.0374927 
R^2.......................: 0.16644 
Adjusted r^2..............: 0.126535 
Sample size of AE DB......: 2388 
Sample size of model......: 395 
Missing data %............: 83.45896 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + Med.Statin.LLD + 
    Med.all.antiplatelet + CAD_history + IL6_pg_ug_2015_rank, 
    data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale  Hypertension.compositeyes  
                  0.77356                   -0.13097                   -0.01268                    0.22471                   -0.21870  
   DiabetesStatusDiabetes          Med.Statin.LLDyes    Med.all.antiplateletyes                CAD_history        IL6_pg_ug_2015_rank  
                 -0.28091                   -0.20124                    0.37211                    0.21861                    0.24395  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.55335 -0.65341 -0.03949  0.65521  2.69818 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.554051   0.977225   1.590   0.1126    
currentDF[, TRAIT]        -0.130423   0.048350  -2.697   0.0073 ** 
Age                       -0.015344   0.006137  -2.500   0.0128 *  
Gendermale                 0.213022   0.106539   1.999   0.0463 *  
Hypertension.compositeyes -0.231524   0.143610  -1.612   0.1078    
DiabetesStatusDiabetes    -0.267855   0.119386  -2.244   0.0254 *  
SmokerCurrentyes          -0.062128   0.103707  -0.599   0.5495    
Med.Statin.LLDyes         -0.215775   0.110095  -1.960   0.0508 .  
Med.all.antiplateletyes    0.306341   0.166546   1.839   0.0667 .  
GFR_MDRD                  -0.002438   0.002715  -0.898   0.3696    
BMI                       -0.008989   0.013030  -0.690   0.4907    
CAD_history                0.238812   0.110205   2.167   0.0309 *  
Stroke_history             0.101300   0.100583   1.007   0.3145    
Peripheral.interv         -0.184149   0.127330  -1.446   0.1489    
stenose50-70%             -0.019812   0.710604  -0.028   0.9778    
stenose70-90%             -0.064875   0.667296  -0.097   0.9226    
stenose90-99%             -0.098145   0.664491  -0.148   0.8827    
stenose100% (Occlusion)   -0.945321   0.825343  -1.145   0.2528    
IL6_pg_ug_2015_rank        0.241475   0.046528   5.190 3.46e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9273 on 373 degrees of freedom
Multiple R-squared:  0.1722,    Adjusted R-squared:  0.1323 
F-statistic: 4.311 on 18 and 373 DF,  p-value: 2.081e-08

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.130423 
Standard error............: 0.04835 
Odds ratio (effect size)..: 0.878 
Lower 95% CI..............: 0.798 
Upper 95% CI..............: 0.965 
T-value...................: -2.697469 
P-value...................: 0.007304015 
R^2.......................: 0.172223 
Adjusted r^2..............: 0.132277 
Sample size of AE DB......: 2388 
Sample size of model......: 392 
Missing data %............: 83.58459 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + DiabetesStatus + Med.Statin.LLD + Med.all.antiplatelet + 
    CAD_history + IL6_pg_ug_2015_rank, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age               Gendermale   DiabetesStatusDiabetes  
               0.347892                -0.130851                -0.009673                 0.291255                -0.237930  
      Med.Statin.LLDyes  Med.all.antiplateletyes              CAD_history      IL6_pg_ug_2015_rank  
              -0.211412                 0.394747                 0.204490                 0.281865  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.71687 -0.67661 -0.01886  0.67777  2.71172 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                1.227389   0.986479   1.244  0.21422    
currentDF[, TRAIT]        -0.139249   0.060068  -2.318  0.02099 *  
Age                       -0.011612   0.006075  -1.911  0.05672 .  
Gendermale                 0.283848   0.106206   2.673  0.00786 ** 
Hypertension.compositeyes -0.190942   0.148027  -1.290  0.19789    
DiabetesStatusDiabetes    -0.217584   0.121942  -1.784  0.07520 .  
SmokerCurrentyes          -0.072471   0.105407  -0.688  0.49218    
Med.Statin.LLDyes         -0.222020   0.111033  -2.000  0.04628 *  
Med.all.antiplateletyes    0.337245   0.167708   2.011  0.04507 *  
GFR_MDRD                  -0.002070   0.002782  -0.744  0.45743    
BMI                       -0.008220   0.013248  -0.620  0.53534    
CAD_history                0.229973   0.111607   2.061  0.04005 *  
Stroke_history             0.113074   0.102710   1.101  0.27166    
Peripheral.interv         -0.131189   0.129629  -1.012  0.31219    
stenose50-70%             -0.164915   0.714212  -0.231  0.81752    
stenose70-90%             -0.127877   0.671601  -0.190  0.84910    
stenose90-99%             -0.159676   0.668379  -0.239  0.81132    
stenose100% (Occlusion)   -1.011506   0.831005  -1.217  0.22431    
IL6_pg_ug_2015_rank        0.276904   0.045999   6.020 4.24e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9319 on 367 degrees of freedom
Multiple R-squared:  0.1718,    Adjusted R-squared:  0.1312 
F-statistic: 4.229 on 18 and 367 DF,  p-value: 3.499e-08

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.139249 
Standard error............: 0.060068 
Odds ratio (effect size)..: 0.87 
Lower 95% CI..............: 0.773 
Upper 95% CI..............: 0.979 
T-value...................: -2.318182 
P-value...................: 0.02098847 
R^2.......................: 0.171797 
Adjusted r^2..............: 0.131177 
Sample size of AE DB......: 2388 
Sample size of model......: 386 
Missing data %............: 83.83585 

Analysis of IL6_pg_ug_2015_rank.

- processing Macrophages_rank
attempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + 
    GFR_MDRD + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank + Hypertension.composite, data = currentDF)

Coefficients:
              (Intercept)         currentDF[, TRAIT]                        Age                 Gendermale     DiabetesStatusDiabetes  
                2.247e-17                  1.038e-17                 -4.087e-19                  9.218e-19                 -3.176e-18  
         SmokerCurrentyes          Med.Statin.LLDyes                   GFR_MDRD                CAD_history             Stroke_history  
                8.553e-18                 -7.241e-18                 -2.651e-19                 -1.348e-17                  2.148e-17  
        Peripheral.interv              stenose50-70%              stenose70-90%              stenose90-99%    stenose100% (Occlusion)  
               -1.680e-17                 -9.075e-19                  2.774e-17                  2.150e-17                  6.606e-17  
            stenose50-99%              stenose70-99%        IL6_pg_ug_2015_rank  Hypertension.compositeyes  
                1.901e-18                 -1.685e-17                  1.000e+00                  6.901e-18  
essentially perfect fit: summary may be unreliable

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
       Min         1Q     Median         3Q        Max 
-2.048e-16 -2.335e-17 -1.240e-18  2.056e-17  1.998e-15 

Coefficients:
                            Estimate Std. Error    t value Pr(>|t|)    
(Intercept)               -1.465e-16  5.066e-17 -2.891e+00  0.00393 ** 
currentDF[, TRAIT]        -2.077e-17  2.546e-18 -8.157e+00 1.05e-15 ***
Age                        8.697e-19  3.119e-19  2.788e+00  0.00540 ** 
Gendermale                -2.298e-18  5.575e-18 -4.120e-01  0.68023    
Hypertension.compositeyes  2.249e-17  7.759e-18  2.898e+00  0.00383 ** 
DiabetesStatusDiabetes     2.213e-18  6.128e-18  3.610e-01  0.71813    
SmokerCurrentyes          -1.421e-17  5.628e-18 -2.525e+00  0.01172 *  
Med.Statin.LLDyes          1.420e-17  6.136e-18  2.314e+00  0.02089 *  
Med.all.antiplateletyes   -1.538e-18  8.327e-18 -1.850e-01  0.85347    
GFR_MDRD                   5.806e-19  1.339e-19  4.335e+00 1.61e-05 ***
BMI                        1.866e-18  6.970e-19  2.677e+00  0.00754 ** 
CAD_history                2.443e-17  5.770e-18  4.233e+00 2.52e-05 ***
Stroke_history            -4.160e-17  5.419e-18 -7.677e+00 3.94e-14 ***
Peripheral.interv          3.323e-17  6.658e-18  4.991e+00 7.12e-07 ***
stenose50-70%              2.052e-17  3.667e-17  5.600e-01  0.57593    
stenose70-90%             -3.799e-17  3.547e-17 -1.071e+00  0.28441    
stenose90-99%             -2.632e-17  3.545e-17 -7.420e-01  0.45796    
stenose100% (Occlusion)   -8.577e-17  4.509e-17 -1.902e+00  0.05742 .  
stenose50-99%             -3.117e-18  6.585e-17 -4.700e-02  0.96226    
stenose70-99%              3.375e-17  4.777e-17  7.070e-01  0.47998    
IL6_pg_ug_2015_rank        1.000e+00  2.555e-18  3.913e+17  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 7.856e-17 on 975 degrees of freedom
Multiple R-squared:      1, Adjusted R-squared:      1 
F-statistic: 7.984e+33 on 20 and 975 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Macrophages_rank ' .
essentially perfect fit: summary may be unreliable
Collecting data.
essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliable
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0 
Standard error............: 0 
Odds ratio (effect size)..: 1 
Lower 95% CI..............: 1 
Upper 95% CI..............: 1 
T-value...................: -8.156576 
P-value...................: 1.054821e-15 
R^2.......................: 1 
Adjusted r^2..............: 1 
Sample size of AE DB......: 2388 
Sample size of model......: 996 
Missing data %............: 58.29146 

- processing SMC_rank
attempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense

Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + Hypertension.composite + 
    SmokerCurrent + Med.Statin.LLD + GFR_MDRD + BMI + Stroke_history + 
    Peripheral.interv + stenose + IL6_pg_ug_2015_rank + currentDF[, 
    TRAIT], data = currentDF)

Coefficients:
              (Intercept)                        Age                 Gendermale  Hypertension.compositeyes           SmokerCurrentyes  
               -1.572e-16                  1.286e-18                 -1.660e-18                  2.490e-17                 -1.283e-17  
        Med.Statin.LLDyes                   GFR_MDRD                        BMI             Stroke_history          Peripheral.interv  
                1.490e-17                  5.157e-19                  1.938e-18                 -4.570e-17                  4.006e-17  
            stenose50-70%              stenose70-90%              stenose90-99%    stenose100% (Occlusion)              stenose50-99%  
                1.437e-17                 -5.063e-17                 -3.897e-17                 -1.201e-16                  4.907e-18  
            stenose70-99%        IL6_pg_ug_2015_rank         currentDF[, TRAIT]  
                4.537e-17                  1.000e+00                  2.219e-18  
essentially perfect fit: summary may be unreliable

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
       Min         1Q     Median         3Q        Max 
-3.584e-16 -3.610e-17 -7.100e-18  2.310e-17  6.025e-15 

Coefficients:
                            Estimate Std. Error    t value Pr(>|t|)    
(Intercept)                1.894e-16  1.305e-16  1.451e+00  0.14703    
currentDF[, TRAIT]        -3.614e-17  6.889e-18 -5.246e+00 1.91e-07 ***
Age                       -1.834e-18  8.128e-19 -2.257e+00  0.02426 *  
Gendermale                -7.294e-18  1.454e-17 -5.020e-01  0.61596    
Hypertension.compositeyes -1.927e-17  1.997e-17 -9.650e-01  0.33487    
DiabetesStatusDiabetes    -1.416e-18  1.577e-17 -9.000e-02  0.92851    
SmokerCurrentyes           1.331e-17  1.450e-17  9.180e-01  0.35863    
Med.Statin.LLDyes         -1.450e-17  1.582e-17 -9.160e-01  0.35967    
Med.all.antiplateletyes   -4.570e-18  2.143e-17 -2.130e-01  0.83118    
GFR_MDRD                  -4.874e-19  3.451e-19 -1.412e+00  0.15818    
BMI                       -1.694e-18  1.796e-18 -9.430e-01  0.34589    
CAD_history               -2.023e-17  1.486e-17 -1.361e+00  0.17378    
Stroke_history             4.370e-17  1.394e-17  3.134e+00  0.00177 ** 
Peripheral.interv         -3.586e-17  1.722e-17 -2.082e+00  0.03761 *  
stenose50-70%             -8.171e-18  9.437e-17 -8.700e-02  0.93102    
stenose70-90%              5.711e-17  9.130e-17  6.260e-01  0.53177    
stenose90-99%              4.816e-17  9.126e-17  5.280e-01  0.59780    
stenose100% (Occlusion)    9.823e-17  1.160e-16  8.470e-01  0.39743    
stenose50-99%              3.366e-17  1.696e-16  1.980e-01  0.84270    
stenose70-99%             -1.688e-17  1.229e-16 -1.370e-01  0.89078    
IL6_pg_ug_2015_rank        1.000e+00  6.640e-18  1.506e+17  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.021e-16 on 971 degrees of freedom
Multiple R-squared:      1, Adjusted R-squared:      1 
F-statistic: 1.202e+33 on 20 and 971 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' SMC_rank ' .
essentially perfect fit: summary may be unreliable
Collecting data.
essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliable
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: 0 
Standard error............: 0 
Odds ratio (effect size)..: 1 
Lower 95% CI..............: 1 
Upper 95% CI..............: 1 
T-value...................: -5.246188 
P-value...................: 1.90645e-07 
R^2.......................: 1 
Adjusted r^2..............: 1 
Sample size of AE DB......: 2388 
Sample size of model......: 992 
Missing data %............: 58.45896 

- processing VesselDensity_rank
attempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + 
    Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age               Gendermale   DiabetesStatusDiabetes  
             -1.378e-16                1.402e-17                7.842e-19                4.466e-20                1.115e-17  
       SmokerCurrentyes        Med.Statin.LLDyes  Med.all.antiplateletyes                 GFR_MDRD                      BMI  
             -1.803e-17                1.125e-17                3.124e-18                7.339e-19                1.730e-18  
            CAD_history        Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  
              2.810e-17                4.228e-17                1.752e-17               -4.549e-17               -3.570e-17  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%      IL6_pg_ug_2015_rank  
             -1.115e-16               -1.037e-17                7.471e-17                1.000e+00  
essentially perfect fit: summary may be unreliable

Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
       Min         1Q     Median         3Q        Max 
-6.332e-15 -2.720e-17  6.100e-18  3.790e-17  3.518e-16 

Coefficients:
                            Estimate Std. Error    t value Pr(>|t|)    
(Intercept)               -1.550e-16  1.437e-16 -1.079e+00  0.28107    
currentDF[, TRAIT]         1.511e-17  7.444e-18  2.030e+00  0.04269 *  
Age                        9.594e-19  8.973e-19  1.069e+00  0.28525    
Gendermale                -8.395e-19  1.605e-17 -5.200e-02  0.95831    
Hypertension.compositeyes  2.367e-17  2.235e-17  1.059e+00  0.28998    
DiabetesStatusDiabetes     1.146e-17  1.806e-17  6.350e-01  0.52580    
SmokerCurrentyes          -1.388e-17  1.631e-17 -8.510e-01  0.39497    
Med.Statin.LLDyes          1.210e-17  1.755e-17  6.900e-01  0.49068    
Med.all.antiplateletyes    4.826e-18  2.430e-17  1.990e-01  0.84265    
GFR_MDRD                   7.155e-19  3.899e-19  1.835e+00  0.06682 .  
BMI                        1.311e-18  1.998e-18  6.560e-01  0.51187    
CAD_history                2.138e-17  1.674e-17  1.277e+00  0.20194    
Stroke_history            -4.574e-17  1.566e-17 -2.921e+00  0.00358 ** 
Peripheral.interv          3.700e-17  1.960e-17  1.888e+00  0.05937 .  
stenose50-70%              3.483e-17  1.030e-16  3.380e-01  0.73526    
stenose70-90%             -3.343e-17  9.918e-17 -3.370e-01  0.73619    
stenose90-99%             -2.482e-17  9.905e-17 -2.510e-01  0.80223    
stenose100% (Occlusion)   -7.879e-17  1.260e-16 -6.250e-01  0.53183    
stenose50-99%             -1.041e-17  1.838e-16 -5.700e-02  0.95482    
stenose70-99%              7.479e-17  1.477e-16  5.060e-01  0.61273    
IL6_pg_ug_2015_rank        1.000e+00  7.333e-18  1.364e+17  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.192e-16 on 909 degrees of freedom
Multiple R-squared:      1, Adjusted R-squared:      1 
F-statistic: 9.677e+32 on 20 and 909 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
essentially perfect fit: summary may be unreliable
Collecting data.
essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliable
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: 0 
Standard error............: 0 
Odds ratio (effect size)..: 1 
Lower 95% CI..............: 1 
Upper 95% CI..............: 1 
T-value...................: 2.029581 
P-value...................: 0.04268999 
R^2.......................: 1 
Adjusted r^2..............: 1 
Sample size of AE DB......: 2388 
Sample size of model......: 930 
Missing data %............: 61.05528 

Analysis of IL6R_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age        Med.Statin.LLDyes                 GFR_MDRD  
               0.150100                 0.101725                -0.008416                -0.342529                -0.002550  
      Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
               0.257582                 0.589170                 0.828423                 0.959326                 0.531597  
          stenose50-99%            stenose70-99%      IL6_pg_ug_2015_rank  
               0.613400                 0.005560                 0.347977  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.44459 -0.60814 -0.00894  0.57895  2.91573 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.062717   0.620798   0.101 0.919551    
currentDF[, TRAIT]         0.105434   0.029748   3.544 0.000413 ***
Age                       -0.007985   0.003669  -2.176 0.029799 *  
Gendermale                -0.078319   0.065425  -1.197 0.231575    
Hypertension.compositeyes  0.113763   0.089867   1.266 0.205858    
DiabetesStatusDiabetes    -0.066427   0.071428  -0.930 0.352613    
SmokerCurrentyes           0.027958   0.065807   0.425 0.671041    
Med.Statin.LLDyes         -0.342026   0.071458  -4.786 1.97e-06 ***
Med.all.antiplateletyes    0.057483   0.097149   0.592 0.554192    
GFR_MDRD                  -0.002406   0.001591  -1.512 0.130914    
BMI                       -0.002148   0.008305  -0.259 0.795963    
CAD_history               -0.019022   0.067382  -0.282 0.777780    
Stroke_history             0.001197   0.062994   0.019 0.984839    
Peripheral.interv          0.255591   0.078167   3.270 0.001115 ** 
stenose50-70%              0.617712   0.467816   1.320 0.187017    
stenose70-90%              0.850410   0.454842   1.870 0.061838 .  
stenose90-99%              0.974101   0.454624   2.143 0.032396 *  
stenose100% (Occlusion)    0.569253   0.555373   1.025 0.305630    
stenose50-99%              0.624106   0.781627   0.798 0.424799    
stenose70-99%              0.028999   0.584347   0.050 0.960430    
IL6_pg_ug_2015_rank        0.347123   0.029674  11.698  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9007 on 943 degrees of freedom
Multiple R-squared:  0.1997,    Adjusted R-squared:  0.1827 
F-statistic: 11.76 on 20 and 943 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: 0.105434 
Standard error............: 0.029748 
Odds ratio (effect size)..: 1.111 
Lower 95% CI..............: 1.048 
Upper 95% CI..............: 1.178 
T-value...................: 3.54422 
P-value...................: 0.0004131063 
R^2.......................: 0.19969 
Adjusted r^2..............: 0.182716 
Sample size of AE DB......: 2388 
Sample size of model......: 964 
Missing data %............: 59.63149 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age        Med.Statin.LLDyes                 GFR_MDRD  
               0.011860                 0.141020                -0.006095                -0.324391                -0.002439  
      Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
               0.248431                 0.551901                 0.793695                 0.908083                 0.443882  
          stenose50-99%            stenose70-99%      IL6_pg_ug_2015_rank  
               0.493076                -0.034770                 0.380503  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.46700 -0.63753 -0.00906  0.56995  3.03699 

Coefficients:
                           Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -0.062669   0.618832  -0.101  0.91936    
currentDF[, TRAIT]         0.140772   0.031152   4.519 7.01e-06 ***
Age                       -0.006146   0.003699  -1.662  0.09693 .  
Gendermale                -0.009755   0.065906  -0.148  0.88236    
Hypertension.compositeyes  0.107174   0.089522   1.197  0.23154    
DiabetesStatusDiabetes    -0.073263   0.071166  -1.029  0.30352    
SmokerCurrentyes           0.007015   0.065595   0.107  0.91486    
Med.Statin.LLDyes         -0.325020   0.071302  -4.558 5.84e-06 ***
Med.all.antiplateletyes    0.060764   0.096746   0.628  0.53011    
GFR_MDRD                  -0.002494   0.001587  -1.572  0.11630    
BMI                       -0.002236   0.008283  -0.270  0.78724    
CAD_history               -0.020546   0.067180  -0.306  0.75980    
Stroke_history             0.006106   0.062756   0.097  0.92251    
Peripheral.interv          0.251735   0.078277   3.216  0.00134 ** 
stenose50-70%              0.572437   0.465907   1.229  0.21951    
stenose70-90%              0.814682   0.452975   1.799  0.07242 .  
stenose90-99%              0.923564   0.452837   2.040  0.04168 *  
stenose100% (Occlusion)    0.470060   0.553315   0.850  0.39580    
stenose50-99%              0.482530   0.778970   0.619  0.53577    
stenose70-99%             -0.010458   0.581872  -0.018  0.98566    
IL6_pg_ug_2015_rank        0.380070   0.029873  12.723  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8966 on 939 degrees of freedom
Multiple R-squared:  0.2058,    Adjusted R-squared:  0.1889 
F-statistic: 12.17 on 20 and 939 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: 0.140772 
Standard error............: 0.031152 
Odds ratio (effect size)..: 1.151 
Lower 95% CI..............: 1.083 
Upper 95% CI..............: 1.224 
T-value...................: 4.518919 
P-value...................: 7.008123e-06 
R^2.......................: 0.205808 
Adjusted r^2..............: 0.188892 
Sample size of AE DB......: 2388 
Sample size of model......: 960 
Missing data %............: 59.79899 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Med.Statin.LLD + GFR_MDRD + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]                      Age        Med.Statin.LLDyes                 GFR_MDRD  
               0.140681                 0.082137                -0.009974                -0.325309                -0.002484  
      Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
               0.245491                 0.685825                 0.948039                 1.053760                 0.595197  
          stenose50-99%            stenose70-99%      IL6_pg_ug_2015_rank  
               0.649564                 0.200915                 0.361521  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.43521 -0.61488 -0.03731  0.57256  2.95720 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                0.1224769  0.6354987   0.193  0.84722    
currentDF[, TRAIT]         0.0838526  0.0315461   2.658  0.00800 ** 
Age                       -0.0099916  0.0038138  -2.620  0.00895 ** 
Gendermale                -0.0701012  0.0679394  -1.032  0.30244    
Hypertension.compositeyes  0.1130800  0.0934662   1.210  0.22666    
DiabetesStatusDiabetes    -0.0899933  0.0760878  -1.183  0.23723    
SmokerCurrentyes          -0.0001862  0.0688784  -0.003  0.99784    
Med.Statin.LLDyes         -0.3216068  0.0737798  -4.359 1.46e-05 ***
Med.all.antiplateletyes    0.0254133  0.1024816   0.248  0.80421    
GFR_MDRD                  -0.0024681  0.0016782  -1.471  0.14174    
BMI                       -0.0013735  0.0086073  -0.160  0.87326    
CAD_history               -0.0400880  0.0705725  -0.568  0.57015    
Stroke_history            -0.0009273  0.0657583  -0.014  0.98875    
Peripheral.interv          0.2442989  0.0830145   2.943  0.00334 ** 
stenose50-70%              0.7075552  0.4734750   1.494  0.13543    
stenose70-90%              0.9615837  0.4584673   2.097  0.03624 *  
stenose90-99%              1.0614252  0.4579661   2.318  0.02070 *  
stenose100% (Occlusion)    0.6114586  0.5598014   1.092  0.27501    
stenose50-99%              0.6547144  0.7870557   0.832  0.40572    
stenose70-99%              0.2400900  0.6446638   0.372  0.70967    
IL6_pg_ug_2015_rank        0.3607180  0.0307143  11.744  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.9068 on 878 degrees of freedom
Multiple R-squared:  0.1948,    Adjusted R-squared:  0.1765 
F-statistic: 10.62 on 20 and 878 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: 0.083853 
Standard error............: 0.031546 
Odds ratio (effect size)..: 1.087 
Lower 95% CI..............: 1.022 
Upper 95% CI..............: 1.157 
T-value...................: 2.658102 
P-value...................: 0.008001116 
R^2.......................: 0.194845 
Adjusted r^2..............: 0.176505 
Sample size of AE DB......: 2388 
Sample size of model......: 899 
Missing data %............: 62.35343 

Analysis of MCP1_pg_ug_2015_rank.

- processing Macrophages_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD + 
    stenose + IL6_pg_ug_2015_rank, data = currentDF)

Coefficients:
            (Intercept)       currentDF[, TRAIT]        Med.Statin.LLDyes            stenose50-70%            stenose70-90%  
               -0.16111                 -0.10144                 -0.09883                  0.33742                  0.30463  
          stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%      IL6_pg_ug_2015_rank  
                0.17735                 -0.39888                  0.80246                  0.61182                  0.47786  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.26224 -0.64187 -0.09494  0.63236  2.77380 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -0.0672862  0.5684358  -0.118 0.905798    
currentDF[, TRAIT]        -0.1047256  0.0285550  -3.668 0.000258 ***
Age                       -0.0000102  0.0035026  -0.003 0.997676    
Gendermale                 0.0684751  0.0625268   1.095 0.273730    
Hypertension.compositeyes -0.1160757  0.0870115  -1.334 0.182506    
DiabetesStatusDiabetes    -0.0335692  0.0687372  -0.488 0.625398    
SmokerCurrentyes          -0.0498207  0.0631379  -0.789 0.430259    
Med.Statin.LLDyes         -0.1017569  0.0689928  -1.475 0.140565    
Med.all.antiplateletyes   -0.0223070  0.0933974  -0.239 0.811280    
GFR_MDRD                   0.0005390  0.0015035   0.358 0.720054    
BMI                       -0.0014093  0.0078166  -0.180 0.856961    
CAD_history                0.0272658  0.0647784   0.421 0.673915    
Stroke_history             0.0331656  0.0607674   0.546 0.585343    
Peripheral.interv          0.0861408  0.0746907   1.153 0.249070    
stenose50-70%              0.2914923  0.4112495   0.709 0.478620    
stenose70-90%              0.2756693  0.3977808   0.693 0.488463    
stenose90-99%              0.1523826  0.3975690   0.383 0.701591    
stenose100% (Occlusion)   -0.4551263  0.5056162  -0.900 0.368267    
stenose50-99%              0.7737669  0.7385155   1.048 0.295022    
stenose70-99%              0.5838603  0.5356743   1.090 0.276003    
IL6_pg_ug_2015_rank        0.4787064  0.0287679  16.640  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.881 on 974 degrees of freedom
Multiple R-squared:   0.24, Adjusted R-squared:  0.2244 
F-statistic: 15.38 on 20 and 974 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Macrophages_rank 
Effect size...............: -0.104726 
Standard error............: 0.028555 
Odds ratio (effect size)..: 0.901 
Lower 95% CI..............: 0.852 
Upper 95% CI..............: 0.952 
T-value...................: -3.667503 
P-value...................: 0.0002581885 
R^2.......................: 0.239978 
Adjusted r^2..............: 0.224372 
Sample size of AE DB......: 2388 
Sample size of model......: 995 
Missing data %............: 58.33333 

- processing SMC_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ Med.Statin.LLD + IL6_pg_ug_2015_rank, 
    data = currentDF)

Coefficients:
        (Intercept)    Med.Statin.LLDyes  IL6_pg_ug_2015_rank  
            0.09823             -0.11500              0.46568  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
     Min       1Q   Median       3Q      Max 
-2.33826 -0.64411 -0.09313  0.64652  2.70649 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -0.0850675  0.5739917  -0.148   0.8822    
currentDF[, TRAIT]        -0.0211859  0.0303105  -0.699   0.4847    
Age                        0.0006778  0.0035764   0.190   0.8497    
Gendermale                 0.0454935  0.0639174   0.712   0.4768    
Hypertension.compositeyes -0.1192407  0.0877809  -1.358   0.1747    
DiabetesStatusDiabetes    -0.0316131  0.0693538  -0.456   0.6486    
SmokerCurrentyes          -0.0387297  0.0637439  -0.608   0.5436    
Med.Statin.LLDyes         -0.1167283  0.0697194  -1.674   0.0944 .  
Med.all.antiplateletyes   -0.0130987  0.0942061  -0.139   0.8894    
GFR_MDRD                   0.0004897  0.0015188   0.322   0.7472    
BMI                       -0.0014803  0.0078944  -0.188   0.8513    
CAD_history                0.0226080  0.0654141   0.346   0.7297    
Stroke_history             0.0246322  0.0612970   0.402   0.6879    
Peripheral.interv          0.0951718  0.0757309   1.257   0.2092    
stenose50-70%              0.3062128  0.4148356   0.738   0.4606    
stenose70-90%              0.2699157  0.4013374   0.673   0.5014    
stenose90-99%              0.1487390  0.4011992   0.371   0.7109    
stenose100% (Occlusion)   -0.4130198  0.5100737  -0.810   0.4183    
stenose50-99%              0.7988378  0.7454035   1.072   0.2841    
stenose70-99%              0.5793423  0.5403134   1.072   0.2839    
IL6_pg_ug_2015_rank        0.4667285  0.0292849  15.937   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8883 on 970 degrees of freedom
Multiple R-squared:  0.2298,    Adjusted R-squared:  0.214 
F-statistic: 14.47 on 20 and 970 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: SMC_rank 
Effect size...............: -0.021186 
Standard error............: 0.030311 
Odds ratio (effect size)..: 0.979 
Lower 95% CI..............: 0.923 
Upper 95% CI..............: 1.039 
T-value...................: -0.698961 
P-value...................: 0.4847442 
R^2.......................: 0.229831 
Adjusted r^2..............: 0.213951 
Sample size of AE DB......: 2388 
Sample size of model......: 991 
Missing data %............: 58.50084 

- processing VesselDensity_rank


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Med.Statin.LLD + 
    IL6_pg_ug_2015_rank, data = currentDF)

Coefficients:
        (Intercept)   currentDF[, TRAIT]    Med.Statin.LLDyes  IL6_pg_ug_2015_rank  
             0.1059              -0.1129              -0.1340               0.4638  


Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age + 
    Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
    CAD_history + Stroke_history + Peripheral.interv + stenose + 
    IL6_pg_ug_2015_rank, data = currentDF)

Residuals:
    Min      1Q  Median      3Q     Max 
-2.2114 -0.6422 -0.1143  0.6644  2.8347 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -0.1123560  0.5830692  -0.193 0.847239    
currentDF[, TRAIT]        -0.1087557  0.0302044  -3.601 0.000335 ***
Age                        0.0013327  0.0036430   0.366 0.714580    
Gendermale                 0.0812631  0.0651062   1.248 0.212293    
Hypertension.compositeyes -0.1166860  0.0906341  -1.287 0.198269    
DiabetesStatusDiabetes    -0.0222591  0.0732448  -0.304 0.761274    
SmokerCurrentyes          -0.0389012  0.0661475  -0.588 0.556613    
Med.Statin.LLDyes         -0.1356726  0.0713394  -1.902 0.057515 .  
Med.all.antiplateletyes    0.0298707  0.0985617   0.303 0.761909    
GFR_MDRD                   0.0004300  0.0015827   0.272 0.785909    
BMI                       -0.0008913  0.0081026  -0.110 0.912433    
CAD_history                0.0326170  0.0679685   0.480 0.631426    
Stroke_history             0.0385906  0.0635076   0.608 0.543569    
Peripheral.interv          0.0770844  0.0794949   0.970 0.332466    
stenose50-70%              0.1688723  0.4175091   0.404 0.685958    
stenose70-90%              0.1751473  0.4021881   0.435 0.663313    
stenose90-99%              0.0669127  0.4016549   0.167 0.867728    
stenose100% (Occlusion)   -0.4970748  0.5107978  -0.973 0.330746    
stenose50-99%              0.7449020  0.7452457   1.000 0.317800    
stenose70-99%              0.3718812  0.5989682   0.621 0.534841    
IL6_pg_ug_2015_rank        0.4673873  0.0298681  15.648  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.8889 on 908 degrees of freedom
Multiple R-squared:  0.245, Adjusted R-squared:  0.2284 
F-statistic: 14.73 on 20 and 908 DF,  p-value: < 2.2e-16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.

We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: VesselDensity_rank 
Effect size...............: -0.108756 
Standard error............: 0.030204 
Odds ratio (effect size)..: 0.897 
Lower 95% CI..............: 0.845 
Upper 95% CI..............: 0.952 
T-value...................: -3.600662 
P-value...................: 0.0003346057 
R^2.......................: 0.244999 
Adjusted r^2..............: 0.228369 
Sample size of AE DB......: 2388 
Sample size of model......: 929 
Missing data %............: 61.09715 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5rank) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}

Analysis of IL6_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Hypertension.composite + DiabetesStatus + GFR_MDRD + Stroke_history + 
    Peripheral.interv, family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]  Hypertension.compositeyes     DiabetesStatusDiabetes                   GFR_MDRD  
                   1.1271                     0.2358                     0.4947                    -0.4908                    -0.0128  
           Stroke_history          Peripheral.interv  
                  -0.4847                    -0.4380  

Degrees of Freedom: 370 Total (i.e. Null);  364 Residual
Null Deviance:      506.1 
Residual Deviance: 485.6    AIC: 499.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0991  -1.2044   0.7779   1.0251   1.7551  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -0.154564   2.239780  -0.069   0.9450  
currentDF[, PROTEIN]       0.262668   0.119003   2.207   0.0273 *
Age                        0.008433   0.014091   0.598   0.5495  
Gendermale                -0.197170   0.250644  -0.787   0.4315  
Hypertension.compositeyes  0.582892   0.344647   1.691   0.0908 .
DiabetesStatusDiabetes    -0.485547   0.280208  -1.733   0.0831 .
SmokerCurrentyes           0.051832   0.240725   0.215   0.8295  
Med.Statin.LLDyes          0.156202   0.254201   0.614   0.5389  
Med.all.antiplateletyes    0.445866   0.396461   1.125   0.2608  
GFR_MDRD                  -0.013723   0.006473  -2.120   0.0340 *
BMI                       -0.007941   0.031195  -0.255   0.7991  
CAD_history               -0.191094   0.251677  -0.759   0.4477  
Stroke_history            -0.543313   0.236626  -2.296   0.0217 *
Peripheral.interv         -0.428498   0.283220  -1.513   0.1303  
stenose50-70%              1.078224   1.554409   0.694   0.4879  
stenose70-90%              0.798269   1.450927   0.550   0.5822  
stenose90-99%              0.422362   1.443382   0.293   0.7698  
stenose100% (Occlusion)    1.759182   1.913401   0.919   0.3579  
IL6_pg_ug_2015_rank        0.009689   0.109895   0.088   0.9297  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 506.13  on 370  degrees of freedom
Residual deviance: 478.11  on 352  degrees of freedom
AIC: 516.11

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.262668 
Standard error............: 0.119003 
Odds ratio (effect size)..: 1.3 
Lower 95% CI..............: 1.03 
Upper 95% CI..............: 1.642 
Z-value...................: 2.207238 
P-value...................: 0.02729742 
Hosmer and Lemeshow r^2...: 0.055356 
Cox and Snell r^2.........: 0.072738 
Nagelkerke's pseudo r^2...: 0.09771 
Sample size of AE DB......: 2388 
Sample size of model......: 371 
Missing data %............: 84.46399 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ DiabetesStatus + 
    Med.all.antiplatelet + Peripheral.interv + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)   DiabetesStatusDiabetes  Med.all.antiplateletyes        Peripheral.interv            stenose50-70%  
                16.9670                   0.5050                   0.8794                  -0.5262                  -0.2168  
          stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
               -16.2556                 -16.5665                   0.4065  

Degrees of Freedom: 370 Total (i.e. Null);  363 Residual
Null Deviance:      376.2 
Residual Deviance: 356.9    AIC: 372.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.20699   0.00025   0.60086   0.71577   1.25310  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.714e+01  2.759e+03   0.006   0.9950  
currentDF[, PROTEIN]      -1.061e-01  1.409e-01  -0.753   0.4514  
Age                        8.344e-03  1.736e-02   0.481   0.6307  
Gendermale                -1.127e-01  3.051e-01  -0.369   0.7118  
Hypertension.compositeyes  9.511e-02  4.163e-01   0.228   0.8193  
DiabetesStatusDiabetes     5.811e-01  3.727e-01   1.559   0.1190  
SmokerCurrentyes           3.757e-01  3.027e-01   1.241   0.2145  
Med.Statin.LLDyes          8.815e-02  3.041e-01   0.290   0.7719  
Med.all.antiplateletyes    8.317e-01  4.272e-01   1.947   0.0516 .
GFR_MDRD                  -2.607e-03  7.804e-03  -0.334   0.7383  
BMI                       -2.999e-02  3.876e-02  -0.774   0.4390  
CAD_history                5.258e-03  3.037e-01   0.017   0.9862  
Stroke_history             2.934e-01  2.987e-01   0.982   0.3260  
Peripheral.interv         -5.299e-01  3.212e-01  -1.649   0.0991 .
stenose50-70%             -2.547e-01  2.949e+03   0.000   0.9999  
stenose70-90%             -1.625e+01  2.759e+03  -0.006   0.9953  
stenose90-99%             -1.660e+01  2.759e+03  -0.006   0.9952  
stenose100% (Occlusion)    3.289e-01  3.355e+03   0.000   0.9999  
IL6_pg_ug_2015_rank       -7.294e-02  1.321e-01  -0.552   0.5807  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 376.24  on 370  degrees of freedom
Residual deviance: 351.74  on 352  degrees of freedom
AIC: 389.74

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.106139 
Standard error............: 0.140924 
Odds ratio (effect size)..: 0.899 
Lower 95% CI..............: 0.682 
Upper 95% CI..............: 1.185 
Z-value...................: -0.753162 
P-value...................: 0.4513528 
Hosmer and Lemeshow r^2...: 0.065117 
Cox and Snell r^2.........: 0.063903 
Nagelkerke's pseudo r^2...: 0.100275 
Sample size of AE DB......: 2388 
Sample size of model......: 371 
Missing data %............: 84.46399 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Hypertension.composite + 
    DiabetesStatus + Stroke_history + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)                 Gendermale  Hypertension.compositeyes     DiabetesStatusDiabetes             Stroke_history  
                   0.2000                     0.9806                     0.7552                    -0.4913                     0.6354  
      IL6_pg_ug_2015_rank  
                   0.4079  

Degrees of Freedom: 370 Total (i.e. Null);  365 Residual
Null Deviance:      353.4 
Residual Deviance: 326  AIC: 338

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.6745   0.3340   0.5000   0.6573   1.1769  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.379e+01  1.025e+03   0.013   0.9893    
currentDF[, PROTEIN]       4.877e-02  1.532e-01   0.318   0.7502    
Age                       -7.928e-03  1.821e-02  -0.435   0.6632    
Gendermale                 1.073e+00  3.031e-01   3.540   0.0004 ***
Hypertension.compositeyes  7.769e-01  4.116e-01   1.887   0.0591 .  
DiabetesStatusDiabetes    -5.348e-01  3.469e-01  -1.542   0.1231    
SmokerCurrentyes           1.027e-02  3.150e-01   0.033   0.9740    
Med.Statin.LLDyes         -3.361e-01  3.461e-01  -0.971   0.3315    
Med.all.antiplateletyes    9.334e-02  5.259e-01   0.177   0.8591    
GFR_MDRD                  -3.589e-03  8.498e-03  -0.422   0.6727    
BMI                        2.784e-02  3.917e-02   0.711   0.4773    
CAD_history               -1.927e-01  3.279e-01  -0.588   0.5568    
Stroke_history             6.793e-01  3.342e-01   2.033   0.0421 *  
Peripheral.interv         -4.333e-02  3.557e-01  -0.122   0.9031    
stenose50-70%             -1.420e+01  1.025e+03  -0.014   0.9889    
stenose70-90%             -1.320e+01  1.025e+03  -0.013   0.9897    
stenose90-99%             -1.343e+01  1.025e+03  -0.013   0.9895    
stenose100% (Occlusion)   -1.409e+01  1.025e+03  -0.014   0.9890    
IL6_pg_ug_2015_rank        3.747e-01  1.477e-01   2.537   0.0112 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 353.45  on 370  degrees of freedom
Residual deviance: 320.00  on 352  degrees of freedom
AIC: 358

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.048772 
Standard error............: 0.15322 
Odds ratio (effect size)..: 1.05 
Lower 95% CI..............: 0.778 
Upper 95% CI..............: 1.418 
Z-value...................: 0.318315 
P-value...................: 0.7502463 
Hosmer and Lemeshow r^2...: 0.094637 
Cox and Snell r^2.........: 0.086215 
Nagelkerke's pseudo r^2...: 0.140347 
Sample size of AE DB......: 2388 
Sample size of model......: 371 
Missing data %............: 84.46399 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    Hypertension.composite + DiabetesStatus + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)                        Age                 Gendermale  Hypertension.compositeyes     DiabetesStatusDiabetes  
                 -3.56538                    0.03604                    0.60110                    0.50040                   -0.49360  
                      BMI  
                  0.05614  

Degrees of Freedom: 370 Total (i.e. Null);  365 Residual
Null Deviance:      426.3 
Residual Deviance: 406.6    AIC: 418.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1170  -1.0237   0.6345   0.7939   1.3578  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                10.536573 623.105263   0.017   0.9865  
currentDF[, PROTEIN]       -0.072502   0.131246  -0.552   0.5807  
Age                         0.029878   0.015474   1.931   0.0535 .
Gendermale                  0.653322   0.269334   2.426   0.0153 *
Hypertension.compositeyes   0.511240   0.360309   1.419   0.1559  
DiabetesStatusDiabetes     -0.533832   0.304331  -1.754   0.0794 .
SmokerCurrentyes            0.072614   0.273203   0.266   0.7904  
Med.Statin.LLDyes          -0.071926   0.288455  -0.249   0.8031  
Med.all.antiplateletyes    -0.469526   0.510870  -0.919   0.3581  
GFR_MDRD                   -0.007508   0.007295  -1.029   0.3034  
BMI                         0.053981   0.034966   1.544   0.1226  
CAD_history                 0.071455   0.292926   0.244   0.8073  
Stroke_history              0.115620   0.268209   0.431   0.6664  
Peripheral.interv           0.380643   0.336888   1.130   0.2585  
stenose50-70%             -12.627468 623.102557  -0.020   0.9838  
stenose70-90%             -12.933723 623.102256  -0.021   0.9834  
stenose90-99%             -12.724102 623.102241  -0.020   0.9837  
stenose100% (Occlusion)   -12.949079 623.103565  -0.021   0.9834  
IL6_pg_ug_2015_rank         0.138068   0.123533   1.118   0.2637  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 426.33  on 370  degrees of freedom
Residual deviance: 399.37  on 352  degrees of freedom
AIC: 437.37

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: IPH 
Effect size...............: -0.072502 
Standard error............: 0.131246 
Odds ratio (effect size)..: 0.93 
Lower 95% CI..............: 0.719 
Upper 95% CI..............: 1.203 
Z-value...................: -0.552416 
P-value...................: 0.5806632 
Hosmer and Lemeshow r^2...: 0.063237 
Cox and Snell r^2.........: 0.070091 
Nagelkerke's pseudo r^2...: 0.102608 
Sample size of AE DB......: 2388 
Sample size of model......: 371 
Missing data %............: 84.46399 

Analysis of MCP1_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ DiabetesStatus + 
    GFR_MDRD + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
           (Intercept)  DiabetesStatusDiabetes                GFR_MDRD          Stroke_history       Peripheral.interv  
               1.64937                -0.62191                -0.01276                -0.48929                -0.51466  

Degrees of Freedom: 394 Total (i.e. Null);  390 Residual
Null Deviance:      536.2 
Residual Deviance: 518.9    AIC: 528.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.7775  -1.2137   0.8015   1.0022   1.7098  

Coefficients:
                           Estimate Std. Error z value Pr(>|z|)  
(Intercept)               -0.678314   2.208953  -0.307   0.7588  
currentDF[, PROTEIN]      -0.126457   0.116030  -1.090   0.2758  
Age                        0.014811   0.013613   1.088   0.2766  
Gendermale                -0.198359   0.241132  -0.823   0.4107  
Hypertension.compositeyes  0.451555   0.321305   1.405   0.1599  
DiabetesStatusDiabetes    -0.684685   0.270340  -2.533   0.0113 *
SmokerCurrentyes           0.150746   0.233998   0.644   0.5194  
Med.Statin.LLDyes         -0.049299   0.249435  -0.198   0.8433  
Med.all.antiplateletyes    0.464052   0.381434   1.217   0.2238  
GFR_MDRD                  -0.011041   0.006211  -1.778   0.0754 .
BMI                        0.005518   0.029397   0.188   0.8511  
CAD_history               -0.047750   0.248409  -0.192   0.8476  
Stroke_history            -0.528106   0.227312  -2.323   0.0202 *
Peripheral.interv         -0.523032   0.283894  -1.842   0.0654 .
stenose50-70%              0.615739   1.616633   0.381   0.7033  
stenose70-90%              0.607629   1.524056   0.399   0.6901  
stenose90-99%              0.287230   1.517562   0.189   0.8499  
stenose100% (Occlusion)    1.446116   2.003565   0.722   0.4704  
IL6_pg_ug_2015_rank        0.040114   0.107454   0.373   0.7089  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 536.17  on 394  degrees of freedom
Residual deviance: 510.01  on 376  degrees of freedom
AIC: 548.01

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.126457 
Standard error............: 0.11603 
Odds ratio (effect size)..: 0.881 
Lower 95% CI..............: 0.702 
Upper 95% CI..............: 1.106 
Z-value...................: -1.089862 
P-value...................: 0.2757739 
Hosmer and Lemeshow r^2...: 0.048787 
Cox and Snell r^2.........: 0.064078 
Nagelkerke's pseudo r^2...: 0.086281 
Sample size of AE DB......: 2388 
Sample size of model......: 395 
Missing data %............: 83.45896 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    DiabetesStatus + SmokerCurrent + Med.all.antiplatelet + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]   DiabetesStatusDiabetes         SmokerCurrentyes  Med.all.antiplateletyes  
                16.8023                  -0.5037                   0.5604                   0.4183                   1.0655  
      Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
                -0.4970                  -0.3808                 -16.1722                 -16.6993                   0.2163  

Degrees of Freedom: 394 Total (i.e. Null);  385 Residual
Null Deviance:      398.1 
Residual Deviance: 361.1    AIC: 381.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
     Min        1Q    Median        3Q       Max  
-2.35134   0.00035   0.53023   0.70233   1.35973  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.727e+01  2.697e+03   0.006 0.994891    
currentDF[, PROTEIN]      -4.904e-01  1.489e-01  -3.295 0.000985 ***
Age                        6.444e-03  1.743e-02   0.370 0.711618    
Gendermale                -1.050e-01  3.052e-01  -0.344 0.730951    
Hypertension.compositeyes  1.497e-01  3.978e-01   0.376 0.706610    
DiabetesStatusDiabetes     6.344e-01  3.780e-01   1.678 0.093348 .  
SmokerCurrentyes           3.929e-01  2.993e-01   1.313 0.189327    
Med.Statin.LLDyes          1.101e-01  3.004e-01   0.367 0.713880    
Med.all.antiplateletyes    1.033e+00  4.268e-01   2.421 0.015469 *  
GFR_MDRD                  -2.116e-03  7.736e-03  -0.274 0.784408    
BMI                       -4.011e-02  3.882e-02  -1.033 0.301595    
CAD_history                2.136e-03  3.022e-01   0.007 0.994360    
Stroke_history             3.252e-01  2.944e-01   1.105 0.269293    
Peripheral.interv         -4.911e-01  3.356e-01  -1.463 0.143391    
stenose50-70%             -2.739e-01  2.884e+03   0.000 0.999924    
stenose70-90%             -1.607e+01  2.697e+03  -0.006 0.995245    
stenose90-99%             -1.661e+01  2.697e+03  -0.006 0.995085    
stenose100% (Occlusion)    1.666e-01  3.255e+03   0.000 0.999959    
IL6_pg_ug_2015_rank       -4.145e-02  1.334e-01  -0.311 0.756111    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 398.07  on 394  degrees of freedom
Residual deviance: 357.98  on 376  degrees of freedom
AIC: 395.98

Number of Fisher Scoring iterations: 16

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.490423 
Standard error............: 0.148854 
Odds ratio (effect size)..: 0.612 
Lower 95% CI..............: 0.457 
Upper 95% CI..............: 0.82 
Z-value...................: -3.294667 
P-value...................: 0.0009853822 
Hosmer and Lemeshow r^2...: 0.100723 
Cox and Snell r^2.........: 0.096525 
Nagelkerke's pseudo r^2...: 0.152014 
Sample size of AE DB......: 2388 
Sample size of model......: 395 
Missing data %............: 83.45896 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Hypertension.composite + BMI + Stroke_history + 
    IL6_pg_ug_2015_rank, family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]                 Gendermale  Hypertension.compositeyes                        BMI  
                 -0.97678                    0.49301                    0.74809                    0.68441                    0.05152  
           Stroke_history        IL6_pg_ug_2015_rank  
                  0.58867                    0.27994  

Degrees of Freedom: 394 Total (i.e. Null);  388 Residual
Null Deviance:      372.1 
Residual Deviance: 334.3    AIC: 348.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.6698   0.2914   0.4662   0.6372   1.6612  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                12.380042 986.394897   0.013  0.98999   
currentDF[, PROTEIN]        0.502902   0.159989   3.143  0.00167 **
Age                         0.004448   0.017753   0.251  0.80218   
Gendermale                  0.807662   0.298791   2.703  0.00687 **
Hypertension.compositeyes   0.801928   0.398141   2.014  0.04399 * 
DiabetesStatusDiabetes     -0.240743   0.350078  -0.688  0.49165   
SmokerCurrentyes            0.028747   0.311494   0.092  0.92647   
Med.Statin.LLDyes          -0.213231   0.346371  -0.616  0.53815   
Med.all.antiplateletyes    -0.145093   0.527572  -0.275  0.78330   
GFR_MDRD                    0.001898   0.008388   0.226  0.82099   
BMI                         0.049489   0.039064   1.267  0.20520   
CAD_history                -0.250635   0.335123  -0.748  0.45453   
Stroke_history              0.594527   0.326713   1.820  0.06880 . 
Peripheral.interv           0.096042   0.374004   0.257  0.79734   
stenose50-70%             -14.663779 986.392765  -0.015  0.98814   
stenose70-90%             -13.156167 986.392596  -0.013  0.98936   
stenose90-99%             -13.579957 986.392567  -0.014  0.98902   
stenose100% (Occlusion)   -13.881091 986.393384  -0.014  0.98877   
IL6_pg_ug_2015_rank         0.255792   0.145952   1.753  0.07967 . 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 372.10  on 394  degrees of freedom
Residual deviance: 325.86  on 376  degrees of freedom
AIC: 363.86

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.502902 
Standard error............: 0.159989 
Odds ratio (effect size)..: 1.654 
Lower 95% CI..............: 1.208 
Upper 95% CI..............: 2.263 
Z-value...................: 3.143363 
P-value...................: 0.001670184 
Hosmer and Lemeshow r^2...: 0.124259 
Cox and Snell r^2.........: 0.110463 
Nagelkerke's pseudo r^2...: 0.181039 
Sample size of AE DB......: 2388 
Sample size of model......: 395 
Missing data %............: 83.45896 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    DiabetesStatus + BMI, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
           (Intercept)                     Age              Gendermale  DiabetesStatusDiabetes                     BMI  
              -2.83856                 0.03202                 0.58621                -0.48794                 0.05657  

Degrees of Freedom: 394 Total (i.e. Null);  390 Residual
Null Deviance:      449.1 
Residual Deviance: 432.8    AIC: 442.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1176  -0.9779   0.6353   0.7916   1.5456  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                10.805144 617.904988   0.017   0.9860  
currentDF[, PROTEIN]        0.049050   0.131219   0.374   0.7085  
Age                         0.023740   0.015089   1.573   0.1156  
Gendermale                  0.577668   0.259736   2.224   0.0261 *
Hypertension.compositeyes   0.428341   0.343972   1.245   0.2130  
DiabetesStatusDiabetes     -0.525303   0.294582  -1.783   0.0746 .
SmokerCurrentyes           -0.017901   0.264461  -0.068   0.9460  
Med.Statin.LLDyes          -0.090069   0.283967  -0.317   0.7511  
Med.all.antiplateletyes    -0.224785   0.452574  -0.497   0.6194  
GFR_MDRD                   -0.005685   0.007069  -0.804   0.4212  
BMI                         0.049055   0.033139   1.480   0.1388  
CAD_history                 0.158330   0.293685   0.539   0.5898  
Stroke_history              0.241351   0.261579   0.923   0.3562  
Peripheral.interv           0.372192   0.343255   1.084   0.2782  
stenose50-70%             -12.662747 617.902663  -0.020   0.9837  
stenose70-90%             -12.874037 617.902369  -0.021   0.9834  
stenose90-99%             -12.670408 617.902351  -0.021   0.9836  
stenose100% (Occlusion)   -12.761985 617.903615  -0.021   0.9835  
IL6_pg_ug_2015_rank         0.086998   0.121825   0.714   0.4752  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 449.12  on 394  degrees of freedom
Residual deviance: 424.14  on 376  degrees of freedom
AIC: 462.14

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.04905 
Standard error............: 0.131219 
Odds ratio (effect size)..: 1.05 
Lower 95% CI..............: 0.812 
Upper 95% CI..............: 1.358 
Z-value...................: 0.373805 
P-value...................: 0.7085493 
Hosmer and Lemeshow r^2...: 0.055615 
Cox and Snell r^2.........: 0.061277 
Nagelkerke's pseudo r^2...: 0.090216 
Sample size of AE DB......: 2388 
Sample size of model......: 395 
Missing data %............: 83.45896 

Analysis of IL6_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + SmokerCurrent + CAD_history + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age         SmokerCurrentyes              CAD_history  
               -1.21906                 -0.10139                  0.01999                  0.39841                  0.25581  
          stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%  
               -1.00567                 -0.50697                 -0.26246                  0.80569                -13.93538  
          stenose70-99%  
               -1.53003  

Degrees of Freedom: 996 Total (i.e. Null);  986 Residual
Null Deviance:      1381 
Residual Deviance: 1349     AIC: 1371

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6147  -1.1333  -0.7964   1.1564   1.6722  

Coefficients: (1 not defined because of singularities)
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                -0.909944   1.321284  -0.689  0.49102   
currentDF[, PROTEIN]       -0.099976   0.066163  -1.511  0.13077   
Age                         0.017028   0.008100   2.102  0.03552 * 
Gendermale                 -0.132850   0.144146  -0.922  0.35672   
Hypertension.compositeyes   0.232383   0.202519   1.147  0.25119   
DiabetesStatusDiabetes     -0.175024   0.159524  -1.097  0.27257   
SmokerCurrentyes            0.413549   0.146703   2.819  0.00482 **
Med.Statin.LLDyes          -0.164639   0.159153  -1.034  0.30092   
Med.all.antiplateletyes    -0.223519   0.217193  -1.029  0.30342   
GFR_MDRD                   -0.001873   0.003492  -0.536  0.59169   
BMI                         0.012482   0.018095   0.690  0.49034   
CAD_history                 0.264314   0.149945   1.763  0.07795 . 
Stroke_history             -0.138429   0.140758  -0.983  0.32538   
Peripheral.interv          -0.183619   0.172919  -1.062  0.28829   
stenose50-70%              -0.939975   0.962505  -0.977  0.32877   
stenose70-90%              -0.485844   0.928836  -0.523  0.60093   
stenose90-99%              -0.236697   0.928355  -0.255  0.79875   
stenose100% (Occlusion)     0.801826   1.244197   0.644  0.51928   
stenose50-99%             -14.007873 368.424975  -0.038  0.96967   
stenose70-99%              -1.453878   1.253623  -1.160  0.24615   
IL6_pg_ug_2015_rank               NA         NA      NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1381.3  on 996  degrees of freedom
Residual deviance: 1340.3  on 977  degrees of freedom
AIC: 1380.3

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.099976 
Standard error............: 0.066163 
Odds ratio (effect size)..: 0.905 
Lower 95% CI..............: 0.795 
Upper 95% CI..............: 1.03 
Z-value...................: -1.511065 
P-value...................: 0.1307718 
Hosmer and Lemeshow r^2...: 0.029706 
Cox and Snell r^2.........: 0.04032 
Nagelkerke's pseudo r^2...: 0.053776 
Sample size of AE DB......: 2388 
Sample size of model......: 997 
Missing data %............: 58.24958 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Hypertension.composite + SmokerCurrent + BMI + Stroke_history, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]  Hypertension.compositeyes           SmokerCurrentyes                        BMI  
                 -0.01981                   -0.29027                    0.34009                    0.44475                    0.03349  
           Stroke_history  
                  0.25226  

Degrees of Freedom: 999 Total (i.e. Null);  994 Residual
Null Deviance:      1017 
Residual Deviance: 993  AIC: 1005

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2838   0.4520   0.6083   0.7205   1.0945  

Coefficients: (1 not defined because of singularities)
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.292e+01  6.481e+02   0.020 0.984089    
currentDF[, PROTEIN]      -2.788e-01  8.257e-02  -3.376 0.000735 ***
Age                        8.333e-03  9.876e-03   0.844 0.398752    
Gendermale                -8.397e-02  1.781e-01  -0.471 0.637300    
Hypertension.compositeyes  3.107e-01  2.330e-01   1.334 0.182365    
DiabetesStatusDiabetes     2.105e-01  2.039e-01   1.033 0.301764    
SmokerCurrentyes           4.917e-01  1.863e-01   2.639 0.008305 ** 
Med.Statin.LLDyes         -1.661e-02  1.955e-01  -0.085 0.932280    
Med.all.antiplateletyes    1.316e-01  2.617e-01   0.503 0.615014    
GFR_MDRD                   4.714e-03  4.302e-03   1.096 0.273176    
BMI                        3.356e-02  2.363e-02   1.420 0.155532    
CAD_history                2.203e-01  1.882e-01   1.171 0.241679    
Stroke_history             2.405e-01  1.765e-01   1.363 0.172966    
Peripheral.interv         -1.820e-02  2.155e-01  -0.084 0.932671    
stenose50-70%             -1.361e+01  6.481e+02  -0.021 0.983249    
stenose70-90%             -1.401e+01  6.481e+02  -0.022 0.982747    
stenose90-99%             -1.406e+01  6.481e+02  -0.022 0.982690    
stenose100% (Occlusion)    4.945e-01  8.183e+02   0.001 0.999518    
stenose50-99%              1.313e-02  1.208e+03   0.000 0.999991    
stenose70-99%             -1.373e+01  6.481e+02  -0.021 0.983103    
IL6_pg_ug_2015_rank               NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1017.22  on 999  degrees of freedom
Residual deviance:  980.18  on 980  degrees of freedom
AIC: 1020.2

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.278762 
Standard error............: 0.08257 
Odds ratio (effect size)..: 0.757 
Lower 95% CI..............: 0.644 
Upper 95% CI..............: 0.89 
Z-value...................: -3.376059 
P-value...................: 0.0007353204 
Hosmer and Lemeshow r^2...: 0.036411 
Cox and Snell r^2.........: 0.036361 
Nagelkerke's pseudo r^2...: 0.056956 
Sample size of AE DB......: 2388 
Sample size of model......: 1000 
Missing data %............: 58.12395 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale        Stroke_history     Peripheral.interv  
              0.5014                0.4505                0.8327                0.3722               -0.6087  

Degrees of Freedom: 999 Total (i.e. Null);  995 Residual
Null Deviance:      1165 
Residual Deviance: 1079     AIC: 1089

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2290  -1.0455   0.6047   0.8064   2.0491  

Coefficients: (1 not defined because of singularities)
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.317e+01  3.905e+02   0.034 0.973093    
currentDF[, PROTEIN]       4.532e-01  7.890e-02   5.744 9.26e-09 ***
Age                        9.621e-03  9.328e-03   1.031 0.302330    
Gendermale                 8.512e-01  1.611e-01   5.282 1.27e-07 ***
Hypertension.compositeyes  6.829e-02  2.349e-01   0.291 0.771250    
DiabetesStatusDiabetes    -1.155e-01  1.839e-01  -0.628 0.529991    
SmokerCurrentyes           1.014e-01  1.710e-01   0.593 0.553366    
Med.Statin.LLDyes         -1.768e-01  1.904e-01  -0.928 0.353200    
Med.all.antiplateletyes    6.371e-02  2.501e-01   0.255 0.798916    
GFR_MDRD                  -2.636e-04  4.098e-03  -0.064 0.948718    
BMI                        3.776e-03  2.044e-02   0.185 0.853460    
CAD_history                7.889e-02  1.751e-01   0.451 0.652297    
Stroke_history             3.768e-01  1.703e-01   2.213 0.026912 *  
Peripheral.interv         -6.136e-01  1.860e-01  -3.299 0.000972 ***
stenose50-70%             -1.353e+01  3.905e+02  -0.035 0.972366    
stenose70-90%             -1.348e+01  3.905e+02  -0.035 0.972456    
stenose90-99%             -1.333e+01  3.905e+02  -0.034 0.972772    
stenose100% (Occlusion)   -1.427e+01  3.905e+02  -0.037 0.970836    
stenose50-99%             -1.498e+01  3.905e+02  -0.038 0.969387    
stenose70-99%             -1.461e+01  3.905e+02  -0.037 0.970150    
IL6_pg_ug_2015_rank               NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1164.5  on 999  degrees of freedom
Residual deviance: 1067.6  on 980  degrees of freedom
AIC: 1107.6

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: 0.453209 
Standard error............: 0.078905 
Odds ratio (effect size)..: 1.573 
Lower 95% CI..............: 1.348 
Upper 95% CI..............: 1.837 
Z-value...................: 5.743761 
P-value...................: 9.259659e-09 
Hosmer and Lemeshow r^2...: 0.083261 
Cox and Snell r^2.........: 0.092407 
Nagelkerke's pseudo r^2...: 0.134327 
Sample size of AE DB......: 2388 
Sample size of model......: 1000 
Missing data %............: 58.12395 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Med.Statin.LLD + 
    BMI + CAD_history + Stroke_history + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)               Gendermale        Med.Statin.LLDyes                      BMI              CAD_history  
                0.16028                  0.59271                 -0.25963                  0.02923                  0.30318  
         Stroke_history            stenose50-70%            stenose70-90%            stenose90-99%  stenose100% (Occlusion)  
                0.25028                 -1.09743                 -0.97086                 -0.65159                 -0.80087  
          stenose50-99%            stenose70-99%  
              -15.27559                  0.59126  

Degrees of Freedom: 998 Total (i.e. Null);  987 Residual
Null Deviance:      1331 
Residual Deviance: 1288     AIC: 1312

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9367  -1.2695   0.8101   0.9793   1.4492  

Coefficients: (1 not defined because of singularities)
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 0.134237   1.491544   0.090   0.9283    
currentDF[, PROTEIN]        0.054522   0.068254   0.799   0.4244    
Age                         0.001619   0.008307   0.195   0.8455    
Gendermale                  0.638876   0.146508   4.361  1.3e-05 ***
Hypertension.compositeyes  -0.108080   0.207201  -0.522   0.6019    
DiabetesStatusDiabetes     -0.123290   0.163538  -0.754   0.4509    
SmokerCurrentyes            0.124934   0.151554   0.824   0.4097    
Med.Statin.LLDyes          -0.249499   0.167274  -1.492   0.1358    
Med.all.antiplateletyes     0.161931   0.221057   0.733   0.4638    
GFR_MDRD                   -0.004974   0.003614  -1.376   0.1687    
BMI                         0.033028   0.018696   1.767   0.0773 .  
CAD_history                 0.310960   0.157200   1.978   0.0479 *  
Stroke_history              0.230621   0.146500   1.574   0.1154    
Peripheral.interv           0.037076   0.178283   0.208   0.8353    
stenose50-70%              -1.011322   1.165248  -0.868   0.3854    
stenose70-90%              -0.904406   1.139736  -0.794   0.4275    
stenose90-99%              -0.590355   1.139754  -0.518   0.6045    
stenose100% (Occlusion)    -0.735447   1.357428  -0.542   0.5880    
stenose50-99%             -15.239630 376.951258  -0.040   0.9678    
stenose70-99%               0.597045   1.580966   0.378   0.7057    
IL6_pg_ug_2015_rank               NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1331.0  on 998  degrees of freedom
Residual deviance: 1283.4  on 979  degrees of freedom
AIC: 1323.4

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.054522 
Standard error............: 0.068254 
Odds ratio (effect size)..: 1.056 
Lower 95% CI..............: 0.924 
Upper 95% CI..............: 1.207 
Z-value...................: 0.798812 
P-value...................: 0.4243993 
Hosmer and Lemeshow r^2...: 0.03579 
Cox and Snell r^2.........: 0.046565 
Nagelkerke's pseudo r^2...: 0.063256 
Sample size of AE DB......: 2388 
Sample size of model......: 999 
Missing data %............: 58.16583 

Analysis of IL6R_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent + 
    CAD_history + stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)                      Age         SmokerCurrentyes              CAD_history            stenose50-70%  
               -0.30345                  0.01695                  0.35191                  0.23377                 -1.63626  
          stenose70-90%            stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
               -1.19502                 -0.95911                  0.12013                -14.62719                 -2.19118  
    IL6_pg_ug_2015_rank  
               -0.10793  

Degrees of Freedom: 964 Total (i.e. Null);  954 Residual
Null Deviance:      1337 
Residual Deviance: 1308     AIC: 1330

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.6427  -1.1294  -0.8126   1.1605   1.6993  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                 0.365860   1.506287   0.243   0.8081  
currentDF[, PROTEIN]        0.035856   0.073333   0.489   0.6249  
Age                         0.012807   0.008312   1.541   0.1233  
Gendermale                 -0.137973   0.147315  -0.937   0.3490  
Hypertension.compositeyes   0.232166   0.204392   1.136   0.2560  
DiabetesStatusDiabetes     -0.220703   0.162318  -1.360   0.1739  
SmokerCurrentyes            0.354163   0.149199   2.374   0.0176 *
Med.Statin.LLDyes          -0.155029   0.163322  -0.949   0.3425  
Med.all.antiplateletyes    -0.281243   0.221170  -1.272   0.2035  
GFR_MDRD                   -0.002650   0.003626  -0.731   0.4648  
BMI                         0.005013   0.018788   0.267   0.7896  
CAD_history                 0.249201   0.152590   1.633   0.1024  
Stroke_history             -0.138158   0.142661  -0.968   0.3328  
Peripheral.interv          -0.198377   0.177818  -1.116   0.2646  
stenose50-70%              -1.511542   1.195399  -1.264   0.2061  
stenose70-90%              -1.140150   1.168415  -0.976   0.3292  
stenose90-99%              -0.897700   1.168584  -0.768   0.4424  
stenose100% (Occlusion)     0.140360   1.431129   0.098   0.9219  
stenose50-99%             -14.655351 369.458835  -0.040   0.9684  
stenose70-99%              -2.056345   1.439602  -1.428   0.1532  
IL6_pg_ug_2015_rank        -0.122276   0.071999  -1.698   0.0895 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1336.9  on 964  degrees of freedom
Residual deviance: 1298.8  on 944  degrees of freedom
AIC: 1340.8

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: 0.035856 
Standard error............: 0.073333 
Odds ratio (effect size)..: 1.037 
Lower 95% CI..............: 0.898 
Upper 95% CI..............: 1.197 
Z-value...................: 0.488951 
P-value...................: 0.6248767 
Hosmer and Lemeshow r^2...: 0.028479 
Cox and Snell r^2.........: 0.038686 
Nagelkerke's pseudo r^2...: 0.051597 
Sample size of AE DB......: 2388 
Sample size of model......: 965 
Missing data %............: 59.58962 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    SmokerCurrent + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes           SmokerCurrentyes        IL6_pg_ug_2015_rank  
                   0.9035                     0.3851                     0.4154                    -0.2787  

Degrees of Freedom: 967 Total (i.e. Null);  964 Residual
Null Deviance:      988.9 
Residual Deviance: 969.5    AIC: 977.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.1999   0.4510   0.6138   0.7199   1.1595  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.319e+01  7.229e+02   0.018 0.985446    
currentDF[, PROTEIN]       9.488e-02  9.151e-02   1.037 0.299848    
Age                        8.079e-03  1.012e-02   0.798 0.424662    
Gendermale                -3.242e-02  1.808e-01  -0.179 0.857720    
Hypertension.compositeyes  3.106e-01  2.344e-01   1.325 0.185201    
DiabetesStatusDiabetes     2.597e-01  2.081e-01   1.248 0.212027    
SmokerCurrentyes           4.712e-01  1.888e-01   2.496 0.012569 *  
Med.Statin.LLDyes          5.820e-03  2.001e-01   0.029 0.976800    
Med.all.antiplateletyes    1.787e-01  2.642e-01   0.676 0.498818    
GFR_MDRD                   3.823e-03  4.435e-03   0.862 0.388617    
BMI                        2.593e-02  2.430e-02   1.067 0.285857    
CAD_history                2.617e-01  1.921e-01   1.363 0.173018    
Stroke_history             2.485e-01  1.784e-01   1.393 0.163631    
Peripheral.interv         -6.451e-02  2.204e-01  -0.293 0.769737    
stenose50-70%             -1.371e+01  7.229e+02  -0.019 0.984872    
stenose70-90%             -1.411e+01  7.229e+02  -0.020 0.984424    
stenose90-99%             -1.415e+01  7.229e+02  -0.020 0.984388    
stenose100% (Occlusion)    4.788e-01  8.767e+02   0.001 0.999564    
stenose50-99%             -6.751e-02  1.250e+03   0.000 0.999957    
stenose70-99%             -1.374e+01  7.229e+02  -0.019 0.984839    
IL6_pg_ug_2015_rank       -3.146e-01  9.023e-02  -3.487 0.000488 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 988.95  on 967  degrees of freedom
Residual deviance: 952.48  on 947  degrees of freedom
AIC: 994.48

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: 0.094877 
Standard error............: 0.091513 
Odds ratio (effect size)..: 1.1 
Lower 95% CI..............: 0.919 
Upper 95% CI..............: 1.316 
Z-value...................: 1.03676 
P-value...................: 0.2998478 
Hosmer and Lemeshow r^2...: 0.03687 
Cox and Snell r^2.........: 0.036967 
Nagelkerke's pseudo r^2...: 0.057762 
Sample size of AE DB......: 2388 
Sample size of model......: 968 
Missing data %............: 59.46399 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Stroke_history + 
    Peripheral.interv + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)           Gendermale       Stroke_history    Peripheral.interv  IL6_pg_ug_2015_rank  
             0.5335               0.7864               0.3842              -0.6161               0.4560  

Degrees of Freedom: 967 Total (i.e. Null);  963 Residual
Null Deviance:      1120 
Residual Deviance: 1040     AIC: 1050

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2182  -1.0330   0.6043   0.7938   1.9895  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.301e+01  4.386e+02   0.030   0.9763    
currentDF[, PROTEIN]      -9.866e-02  8.461e-02  -1.166   0.2436    
Age                        1.064e-02  9.678e-03   1.100   0.2714    
Gendermale                 7.990e-01  1.655e-01   4.827 1.38e-06 ***
Hypertension.compositeyes  5.719e-02  2.396e-01   0.239   0.8113    
DiabetesStatusDiabetes    -7.140e-02  1.882e-01  -0.379   0.7044    
SmokerCurrentyes           1.314e-01  1.751e-01   0.750   0.4532    
Med.Statin.LLDyes         -1.867e-01  1.959e-01  -0.953   0.3405    
Med.all.antiplateletyes    7.462e-02  2.551e-01   0.293   0.7699    
GFR_MDRD                   3.978e-04  4.299e-03   0.093   0.9263    
BMI                       -8.401e-04  2.143e-02  -0.039   0.9687    
CAD_history                7.036e-02  1.788e-01   0.393   0.6940    
Stroke_history             3.898e-01  1.732e-01   2.250   0.0245 *  
Peripheral.interv         -5.916e-01  1.915e-01  -3.090   0.0020 ** 
stenose50-70%             -1.334e+01  4.386e+02  -0.030   0.9757    
stenose70-90%             -1.330e+01  4.386e+02  -0.030   0.9758    
stenose90-99%             -1.313e+01  4.386e+02  -0.030   0.9761    
stenose100% (Occlusion)   -1.411e+01  4.386e+02  -0.032   0.9743    
stenose50-99%             -1.479e+01  4.386e+02  -0.034   0.9731    
stenose70-99%             -1.454e+01  4.386e+02  -0.033   0.9736    
IL6_pg_ug_2015_rank        4.952e-01  8.634e-02   5.735 9.76e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1120.4  on 967  degrees of freedom
Residual deviance: 1028.3  on 947  degrees of freedom
AIC: 1070.3

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.098664 
Standard error............: 0.084608 
Odds ratio (effect size)..: 0.906 
Lower 95% CI..............: 0.768 
Upper 95% CI..............: 1.069 
Z-value...................: -1.166137 
P-value...................: 0.2435592 
Hosmer and Lemeshow r^2...: 0.082253 
Cox and Snell r^2.........: 0.090813 
Nagelkerke's pseudo r^2...: 0.132436 
Sample size of AE DB......: 2388 
Sample size of model......: 968 
Missing data %............: 59.46399 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + GFR_MDRD + CAD_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]            Gendermale              GFR_MDRD           CAD_history  
             0.36958               0.12120               0.61093              -0.00555               0.26123  

Degrees of Freedom: 966 Total (i.e. Null);  962 Residual
Null Deviance:      1288 
Residual Deviance: 1260     AIC: 1270

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9270  -1.2740   0.8058   0.9810   1.4859  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 0.658791   1.540117   0.428   0.6688    
currentDF[, PROTEIN]        0.096366   0.075830   1.271   0.2038    
Age                        -0.002405   0.008572  -0.281   0.7791    
Gendermale                  0.653345   0.149866   4.360  1.3e-05 ***
Hypertension.compositeyes  -0.113781   0.209329  -0.544   0.5867    
DiabetesStatusDiabetes     -0.166184   0.165938  -1.001   0.3166    
SmokerCurrentyes            0.102067   0.154554   0.660   0.5090    
Med.Statin.LLDyes          -0.207534   0.171488  -1.210   0.2262    
Med.all.antiplateletyes     0.101285   0.225730   0.449   0.6536    
GFR_MDRD                   -0.006024   0.003767  -1.599   0.1098    
BMI                         0.021536   0.019381   1.111   0.2665    
CAD_history                 0.339149   0.160490   2.113   0.0346 *  
Stroke_history              0.168929   0.148040   1.141   0.2538    
Peripheral.interv           0.005009   0.183636   0.027   0.9782    
stenose50-70%              -0.712137   1.212748  -0.587   0.5571    
stenose70-90%              -0.725992   1.187466  -0.611   0.5409    
stenose90-99%              -0.434821   1.188064  -0.366   0.7144    
stenose100% (Occlusion)    -0.560313   1.397228  -0.401   0.6884    
stenose50-99%             -15.047936 375.974826  -0.040   0.9681    
stenose70-99%               0.870277   1.613226   0.539   0.5896    
IL6_pg_ug_2015_rank         0.019307   0.074389   0.260   0.7952    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1287.7  on 966  degrees of freedom
Residual deviance: 1242.2  on 946  degrees of freedom
AIC: 1284.2

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: 0.096366 
Standard error............: 0.07583 
Odds ratio (effect size)..: 1.101 
Lower 95% CI..............: 0.949 
Upper 95% CI..............: 1.278 
Z-value...................: 1.270818 
P-value...................: 0.2037934 
Hosmer and Lemeshow r^2...: 0.035318 
Cox and Snell r^2.........: 0.045942 
Nagelkerke's pseudo r^2...: 0.062425 
Sample size of AE DB......: 2388 
Sample size of model......: 967 
Missing data %............: 59.50586 

Analysis of MCP1_pg_ug_2015_rank.

- processing CalcificationPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + SmokerCurrent + Med.Statin.LLD + CAD_history + IL6_pg_ug_2015_rank, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age      SmokerCurrentyes     Med.Statin.LLDyes           CAD_history  
            -1.51542              -0.59564               0.02046               0.42977              -0.24147               0.28995  
 IL6_pg_ug_2015_rank  
             0.18445  

Degrees of Freedom: 995 Total (i.e. Null);  989 Residual
Null Deviance:      1380 
Residual Deviance: 1300     AIC: 1314

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
   Min      1Q  Median      3Q     Max  
-1.880  -1.075  -0.661   1.089   2.137  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                -1.109170   1.345267  -0.824  0.40966    
currentDF[, PROTEIN]       -0.570914   0.079317  -7.198 6.11e-13 ***
Age                         0.019220   0.008374   2.295  0.02171 *  
Gendermale                 -0.117423   0.148383  -0.791  0.42874    
Hypertension.compositeyes   0.173225   0.208445   0.831  0.40595    
DiabetesStatusDiabetes     -0.198676   0.164371  -1.209  0.22677    
SmokerCurrentyes            0.423323   0.151405   2.796  0.00517 ** 
Med.Statin.LLDyes          -0.233261   0.164455  -1.418  0.15608    
Med.all.antiplateletyes    -0.244111   0.222935  -1.095  0.27352    
GFR_MDRD                   -0.001316   0.003604  -0.365  0.71495    
BMI                         0.012374   0.018631   0.664  0.50658    
CAD_history                 0.294365   0.154806   1.902  0.05724 .  
Stroke_history             -0.135470   0.144686  -0.936  0.34912    
Peripheral.interv          -0.134033   0.177869  -0.754  0.45112    
stenose50-70%              -0.797920   0.966890  -0.825  0.40923    
stenose70-90%              -0.350437   0.931769  -0.376  0.70684    
stenose90-99%              -0.163779   0.931216  -0.176  0.86039    
stenose100% (Occlusion)     0.579291   1.254446   0.462  0.64423    
stenose50-99%             -13.596961 357.337235  -0.038  0.96965    
stenose70-99%              -1.058369   1.267398  -0.835  0.40368    
IL6_pg_ug_2015_rank         0.166581   0.077275   2.156  0.03111 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1379.8  on 995  degrees of freedom
Residual deviance: 1283.6  on 975  degrees of freedom
AIC: 1325.6

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CalcificationPlaque 
Effect size...............: -0.570914 
Standard error............: 0.079317 
Odds ratio (effect size)..: 0.565 
Lower 95% CI..............: 0.484 
Upper 95% CI..............: 0.66 
Z-value...................: -7.197909 
P-value...................: 6.114291e-13 
Hosmer and Lemeshow r^2...: 0.069762 
Cox and Snell r^2.........: 0.092123 
Nagelkerke's pseudo r^2...: 0.122868 
Sample size of AE DB......: 2388 
Sample size of model......: 996 
Missing data %............: 58.29146 

- processing CollagenPlaque


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    SmokerCurrent + BMI + Stroke_history + IL6_pg_ug_2015_rank, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]      SmokerCurrentyes                   BMI        Stroke_history   IL6_pg_ug_2015_rank  
             0.19963              -0.16122               0.41058               0.03702               0.25107              -0.22211  

Degrees of Freedom: 998 Total (i.e. Null);  993 Residual
Null Deviance:      1017 
Residual Deviance: 991.7    AIC: 1004

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2982   0.4429   0.6106   0.7208   1.1427  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.286e+01  6.472e+02   0.020  0.98415   
currentDF[, PROTEIN]      -1.566e-01  9.042e-02  -1.732  0.08322 . 
Age                        8.602e-03  9.904e-03   0.869  0.38509   
Gendermale                -8.042e-02  1.785e-01  -0.451  0.65235   
Hypertension.compositeyes  2.960e-01  2.331e-01   1.269  0.20427   
DiabetesStatusDiabetes     2.102e-01  2.042e-01   1.029  0.30338   
SmokerCurrentyes           4.921e-01  1.868e-01   2.635  0.00843 **
Med.Statin.LLDyes         -2.945e-02  1.963e-01  -0.150  0.88076   
Med.all.antiplateletyes    1.282e-01  2.617e-01   0.490  0.62412   
GFR_MDRD                   4.973e-03  4.316e-03   1.152  0.24916   
BMI                        3.439e-02  2.382e-02   1.444  0.14880   
CAD_history                2.187e-01  1.887e-01   1.159  0.24633   
Stroke_history             2.435e-01  1.767e-01   1.378  0.16806   
Peripheral.interv         -5.055e-03  2.160e-01  -0.023  0.98132   
stenose50-70%             -1.356e+01  6.472e+02  -0.021  0.98329   
stenose70-90%             -1.397e+01  6.472e+02  -0.022  0.98277   
stenose90-99%             -1.404e+01  6.472e+02  -0.022  0.98270   
stenose100% (Occlusion)    4.210e-01  8.183e+02   0.001  0.99959   
stenose50-99%              1.436e-01  1.205e+03   0.000  0.99990   
stenose70-99%             -1.359e+01  6.472e+02  -0.021  0.98324   
IL6_pg_ug_2015_rank       -2.065e-01  9.187e-02  -2.248  0.02457 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1016.76  on 998  degrees of freedom
Residual deviance:  976.91  on 978  degrees of freedom
AIC: 1018.9

Number of Fisher Scoring iterations: 14

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: CollagenPlaque 
Effect size...............: -0.156637 
Standard error............: 0.090422 
Odds ratio (effect size)..: 0.855 
Lower 95% CI..............: 0.716 
Upper 95% CI..............: 1.021 
Z-value...................: -1.732282 
P-value...................: 0.08322335 
Hosmer and Lemeshow r^2...: 0.039191 
Cox and Snell r^2.........: 0.039102 
Nagelkerke's pseudo r^2...: 0.061231 
Sample size of AE DB......: 2388 
Sample size of model......: 999 
Missing data %............: 58.16583 

- processing Fat10Perc


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + Stroke_history + 
    Peripheral.interv + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
        (Intercept)           Gendermale       Stroke_history    Peripheral.interv  IL6_pg_ug_2015_rank  
             0.5001               0.8299               0.3752              -0.6046               0.4578  

Degrees of Freedom: 998 Total (i.e. Null);  994 Residual
Null Deviance:      1164 
Residual Deviance: 1077     AIC: 1087

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2481  -1.0386   0.6040   0.7981   2.0958  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                13.129786 391.540184   0.034  0.97325    
currentDF[, PROTEIN]       -0.086857   0.085197  -1.019  0.30797    
Age                         0.010120   0.009342   1.083  0.27870    
Gendermale                  0.853643   0.161410   5.289 1.23e-07 ***
Hypertension.compositeyes   0.055646   0.235533   0.236  0.81323    
DiabetesStatusDiabetes     -0.116612   0.184074  -0.634  0.52640    
SmokerCurrentyes            0.099393   0.171329   0.580  0.56183    
Med.Statin.LLDyes          -0.172314   0.191205  -0.901  0.36748    
Med.all.antiplateletyes     0.056685   0.250340   0.226  0.82086    
GFR_MDRD                   -0.000110   0.004104  -0.027  0.97863    
BMI                         0.003309   0.020461   0.162  0.87151    
CAD_history                 0.073364   0.175377   0.418  0.67571    
Stroke_history              0.379259   0.170276   2.227  0.02593 *  
Peripheral.interv          -0.601792   0.186366  -3.229  0.00124 ** 
stenose50-70%             -13.495567 391.538791  -0.034  0.97250    
stenose70-90%             -13.455871 391.538696  -0.034  0.97258    
stenose90-99%             -13.316587 391.538695  -0.034  0.97287    
stenose100% (Occlusion)   -14.307713 391.539436  -0.037  0.97085    
stenose50-99%             -14.904510 391.541255  -0.038  0.96963    
stenose70-99%             -14.542576 391.539632  -0.037  0.97037    
IL6_pg_ug_2015_rank         0.501944   0.089901   5.583 2.36e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1163.9  on 998  degrees of freedom
Residual deviance: 1065.4  on 978  degrees of freedom
AIC: 1107.4

Number of Fisher Scoring iterations: 13

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: Fat10Perc 
Effect size...............: -0.086857 
Standard error............: 0.085197 
Odds ratio (effect size)..: 0.917 
Lower 95% CI..............: 0.776 
Upper 95% CI..............: 1.083 
Z-value...................: -1.019483 
P-value...................: 0.3079737 
Hosmer and Lemeshow r^2...: 0.084585 
Cox and Snell r^2.........: 0.093846 
Nagelkerke's pseudo r^2...: 0.136385 
Sample size of AE DB......: 2388 
Sample size of model......: 999 
Missing data %............: 58.16583 

- processing IPH


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Gender + Med.Statin.LLD + BMI + CAD_history + Stroke_history + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]               Gendermale        Med.Statin.LLDyes                      BMI  
                0.13101                 -0.22463                  0.61172                 -0.30216                  0.02976  
            CAD_history           Stroke_history            stenose50-70%            stenose70-90%            stenose90-99%  
                0.33142                  0.24256                 -1.02994                 -0.92698                 -0.62001  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%      IL6_pg_ug_2015_rank  
               -0.92397                -15.13616                  0.81161                  0.15649  

Degrees of Freedom: 997 Total (i.e. Null);  984 Residual
Null Deviance:      1329 
Residual Deviance: 1276     AIC: 1304

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9893  -1.2497   0.7839   0.9752   1.6000  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                 0.190713   1.500984   0.127  0.89889    
currentDF[, PROTEIN]       -0.227337   0.076272  -2.981  0.00288 ** 
Age                         0.001017   0.008366   0.122  0.90327    
Gendermale                  0.661185   0.147632   4.479 7.51e-06 ***
Hypertension.compositeyes  -0.135490   0.208469  -0.650  0.51574    
DiabetesStatusDiabetes     -0.139422   0.164394  -0.848  0.39639    
SmokerCurrentyes            0.110583   0.152610   0.725  0.46869    
Med.Statin.LLDyes          -0.296861   0.169050  -1.756  0.07908 .  
Med.all.antiplateletyes     0.164792   0.222329   0.741  0.45857    
GFR_MDRD                   -0.005186   0.003636  -1.426  0.15374    
BMI                         0.033370   0.018886   1.767  0.07723 .  
CAD_history                 0.332543   0.158490   2.098  0.03589 *  
Stroke_history              0.236477   0.147225   1.606  0.10822    
Peripheral.interv           0.050247   0.179428   0.280  0.77945    
stenose50-70%              -0.958270   1.172731  -0.817  0.41386    
stenose70-90%              -0.855005   1.147066  -0.745  0.45604    
stenose90-99%              -0.556029   1.147046  -0.485  0.62785    
stenose100% (Occlusion)    -0.839142   1.364150  -0.615  0.53846    
stenose50-99%             -15.103152 374.531247  -0.040  0.96783    
stenose70-99%               0.785866   1.596121   0.492  0.62247    
IL6_pg_ug_2015_rank         0.151521   0.077747   1.949  0.05131 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 1329.1  on 997  degrees of freedom
Residual deviance: 1271.5  on 977  degrees of freedom
AIC: 1313.5

Number of Fisher Scoring iterations: 12

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: IPH 
Effect size...............: -0.227337 
Standard error............: 0.076272 
Odds ratio (effect size)..: 0.797 
Lower 95% CI..............: 0.686 
Upper 95% CI..............: 0.925 
Z-value...................: -2.98059 
P-value...................: 0.002876934 
Hosmer and Lemeshow r^2...: 0.043363 
Cox and Snell r^2.........: 0.056113 
Nagelkerke's pseudo r^2...: 0.076242 
Sample size of AE DB......: 2388 
Sample size of model......: 998 
Missing data %............: 58.2077 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque and serum MCP1, IL6, and IL6R levels and the ‘clinical status’ of the plaque in terms of presence of patients’ symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

  • stroke
  • TIA
  • retinal infarction
  • amaurosis fugax
  • asymptomatic

Model 1

In this model we correct for Age, and Gender.

Natural log-transformed data

First we use the natural-log transformed data.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of IL6_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)          Age  
    0.11643      0.02191  

Degrees of Freedom: 455 Total (i.e. Null);  454 Residual
Null Deviance:      417.3 
Residual Deviance: 414.9    AIC: 418.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0057   0.5380   0.5871   0.6331   0.8358  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)
(Intercept)           0.12920    1.08309   0.119    0.905
currentDF[, PROTEIN] -0.02445    0.11699  -0.209    0.834
Age                   0.02168    0.01420   1.527    0.127
Gendermale            0.14136    0.27357   0.517    0.605

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 417.29  on 455  degrees of freedom
Residual deviance: 414.58  on 452  degrees of freedom
AIC: 422.58

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.024449 
Standard error............: 0.11699 
Odds ratio (effect size)..: 0.976 
Lower 95% CI..............: 0.776 
Upper 95% CI..............: 1.227 
Z-value...................: -0.208979 
P-value...................: 0.8344648 
Hosmer and Lemeshow r^2...: 0.006485 
Cox and Snell r^2.........: 0.005917 
Nagelkerke's pseudo r^2...: 0.009869 
Sample size of AE DB......: 2388 
Sample size of model......: 456 
Missing data %............: 80.90452 

Analysis of MCP1_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN], 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]  
              0.2534                0.3195  

Degrees of Freedom: 555 Total (i.e. Null);  554 Residual
Null Deviance:      479 
Residual Deviance: 473.6    AIC: 477.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2317   0.4736   0.5421   0.6107   0.9570  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)  
(Intercept)          -0.80551    1.13385  -0.710   0.4774  
currentDF[, PROTEIN]  0.34336    0.13841   2.481   0.0131 *
Age                   0.01681    0.01356   1.240   0.2151  
Gendermale           -0.24157    0.27090  -0.892   0.3725  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 478.98  on 555  degrees of freedom
Residual deviance: 471.32  on 552  degrees of freedom
AIC: 479.32

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.343356 
Standard error............: 0.138412 
Odds ratio (effect size)..: 1.41 
Lower 95% CI..............: 1.075 
Upper 95% CI..............: 1.849 
Z-value...................: 2.480677 
P-value...................: 0.01311332 
Hosmer and Lemeshow r^2...: 0.015985 
Cox and Snell r^2.........: 0.013676 
Nagelkerke's pseudo r^2...: 0.023684 
Sample size of AE DB......: 2388 
Sample size of model......: 556 
Missing data %............: 76.71692 

Analysis of IL6_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
             0.69140               0.10908               0.03025              -0.37729  

Degrees of Freedom: 1149 Total (i.e. Null);  1146 Residual
Null Deviance:      790.7 
Residual Deviance: 776.9    AIC: 784.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4157   0.3916   0.4523   0.5168   0.7514  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)   
(Intercept)           0.69140    0.73163   0.945   0.3447   
currentDF[, PROTEIN]  0.10908    0.06495   1.680   0.0931 . 
Age                   0.03025    0.01029   2.938   0.0033 **
Gendermale           -0.37729    0.22148  -1.703   0.0885 . 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 790.69  on 1149  degrees of freedom
Residual deviance: 776.93  on 1146  degrees of freedom
AIC: 784.93

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.109082 
Standard error............: 0.064949 
Odds ratio (effect size)..: 1.115 
Lower 95% CI..............: 0.982 
Upper 95% CI..............: 1.267 
Z-value...................: 1.6795 
P-value...................: 0.09305457 
Hosmer and Lemeshow r^2...: 0.017405 
Cox and Snell r^2.........: 0.011896 
Nagelkerke's pseudo r^2...: 0.023925 
Sample size of AE DB......: 2388 
Sample size of model......: 1150 
Missing data %............: 51.84255 

Analysis of IL6R_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
            -0.10978              -0.12672               0.03285              -0.39487  

Degrees of Freedom: 1150 Total (i.e. Null);  1147 Residual
Null Deviance:      803.5 
Residual Deviance: 787.8    AIC: 795.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3738   0.3926   0.4565   0.5237   0.8748  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)   
(Intercept)          -0.10978    0.71558  -0.153  0.87807   
currentDF[, PROTEIN] -0.12672    0.08779  -1.443  0.14889   
Age                   0.03285    0.01023   3.213  0.00132 **
Gendermale           -0.39487    0.22099  -1.787  0.07397 . 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 803.47  on 1150  degrees of freedom
Residual deviance: 787.76  on 1147  degrees of freedom
AIC: 795.76

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.126723 
Standard error............: 0.08779 
Odds ratio (effect size)..: 0.881 
Lower 95% CI..............: 0.742 
Upper 95% CI..............: 1.046 
Z-value...................: -1.443477 
P-value...................: 0.148886 
Hosmer and Lemeshow r^2...: 0.019561 
Cox and Snell r^2.........: 0.013562 
Nagelkerke's pseudo r^2...: 0.026992 
Sample size of AE DB......: 2388 
Sample size of model......: 1151 
Missing data %............: 51.80067 

Analysis of MCP1_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
             0.46900               0.17995               0.03227              -0.43409  

Degrees of Freedom: 1195 Total (i.e. Null);  1192 Residual
Null Deviance:      826.5 
Residual Deviance: 805.9    AIC: 813.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5337   0.3791   0.4465   0.5159   0.8508  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)   
(Intercept)           0.46900    0.70095   0.669  0.50343   
currentDF[, PROTEIN]  0.17995    0.06837   2.632  0.00848 **
Age                   0.03227    0.01010   3.194  0.00140 **
Gendermale           -0.43409    0.21786  -1.992  0.04632 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 826.52  on 1195  degrees of freedom
Residual deviance: 805.92  on 1192  degrees of freedom
AIC: 813.92

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.179955 
Standard error............: 0.068365 
Odds ratio (effect size)..: 1.197 
Lower 95% CI..............: 1.047 
Upper 95% CI..............: 1.369 
Z-value...................: 2.632248 
P-value...................: 0.008482191 
Hosmer and Lemeshow r^2...: 0.024928 
Cox and Snell r^2.........: 0.01708 
Nagelkerke's pseudo r^2...: 0.03423 
Sample size of AE DB......: 2388 
Sample size of model......: 1196 
Missing data %............: 49.91625 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of IL6_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)          Age  
    0.03354      0.02322  

Degrees of Freedom: 527 Total (i.e. Null);  526 Residual
Null Deviance:      482.2 
Residual Deviance: 479  AIC: 483

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.0886   0.5322   0.5876   0.6383   0.8258  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)  
(Intercept)           0.07050    0.88346   0.080   0.9364  
currentDF[, PROTEIN] -0.04480    0.12075  -0.371   0.7106  
Age                   0.02320    0.01296   1.789   0.0735 .
Gendermale           -0.04751    0.25974  -0.183   0.8549  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 482.18  on 527  degrees of freedom
Residual deviance: 478.81  on 524  degrees of freedom
AIC: 486.81

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.044803 
Standard error............: 0.120747 
Odds ratio (effect size)..: 0.956 
Lower 95% CI..............: 0.755 
Upper 95% CI..............: 1.212 
Z-value...................: -0.37105 
P-value...................: 0.7106006 
Hosmer and Lemeshow r^2...: 0.006985 
Cox and Snell r^2.........: 0.006359 
Nagelkerke's pseudo r^2...: 0.010619 
Sample size of AE DB......: 2388 
Sample size of model......: 528 
Missing data %............: 77.88945 

Analysis of MCP1_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age  
             0.28675               0.37178               0.02121  

Degrees of Freedom: 564 Total (i.e. Null);  562 Residual
Null Deviance:      495.5 
Residual Deviance: 483.5    AIC: 489.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3596   0.4499   0.5370   0.6291   0.9789  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)   
(Intercept)           0.44218    0.89858   0.492  0.62265   
currentDF[, PROTEIN]  0.38662    0.12065   3.205  0.00135 **
Age                   0.02149    0.01316   1.633  0.10250   
Gendermale           -0.23867    0.26432  -0.903  0.36656   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 495.50  on 564  degrees of freedom
Residual deviance: 482.64  on 561  degrees of freedom
AIC: 490.64

Number of Fisher Scoring iterations: 4

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.386619 
Standard error............: 0.120648 
Odds ratio (effect size)..: 1.472 
Lower 95% CI..............: 1.162 
Upper 95% CI..............: 1.865 
Z-value...................: 3.204514 
P-value...................: 0.001352907 
Hosmer and Lemeshow r^2...: 0.02596 
Cox and Snell r^2.........: 0.022509 
Nagelkerke's pseudo r^2...: 0.038546 
Sample size of AE DB......: 2388 
Sample size of model......: 565 
Missing data %............: 76.34003 

Analysis of IL6_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
             0.34611               0.15893               0.03016              -0.37900  

Degrees of Freedom: 1149 Total (i.e. Null);  1146 Residual
Null Deviance:      790.7 
Residual Deviance: 777  AIC: 785

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.4207   0.3921   0.4517   0.5173   0.7538  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)   
(Intercept)           0.34611    0.70495   0.491  0.62345   
currentDF[, PROTEIN]  0.15893    0.09515   1.670  0.09485 . 
Age                   0.03016    0.01029   2.930  0.00339 **
Gendermale           -0.37900    0.22151  -1.711  0.08709 . 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 790.69  on 1149  degrees of freedom
Residual deviance: 776.96  on 1146  degrees of freedom
AIC: 784.96

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.158935 
Standard error............: 0.095151 
Odds ratio (effect size)..: 1.172 
Lower 95% CI..............: 0.973 
Upper 95% CI..............: 1.413 
Z-value...................: 1.670352 
P-value...................: 0.09484966 
Hosmer and Lemeshow r^2...: 0.017371 
Cox and Snell r^2.........: 0.011873 
Nagelkerke's pseudo r^2...: 0.023879 
Sample size of AE DB......: 2388 
Sample size of model......: 1150 
Missing data %............: 51.84255 

Analysis of IL6R_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
(Intercept)          Age   Gendermale  
    0.10526      0.03331     -0.38379  

Degrees of Freedom: 1151 Total (i.e. Null);  1149 Residual
Null Deviance:      803.7 
Residual Deviance: 790.2    AIC: 796.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3792   0.3949   0.4577   0.5226   0.8703  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)   
(Intercept)           0.14037    0.70096   0.200  0.84129   
currentDF[, PROTEIN] -0.11502    0.09484  -1.213  0.22520   
Age                   0.03295    0.01023   3.222  0.00127 **
Gendermale           -0.39123    0.22092  -1.771  0.07658 . 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 803.71  on 1151  degrees of freedom
Residual deviance: 788.73  on 1148  degrees of freedom
AIC: 796.73

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.115023 
Standard error............: 0.094838 
Odds ratio (effect size)..: 0.891 
Lower 95% CI..............: 0.74 
Upper 95% CI..............: 1.073 
Z-value...................: -1.212829 
P-value...................: 0.2251951 
Hosmer and Lemeshow r^2...: 0.018632 
Cox and Snell r^2.........: 0.012915 
Nagelkerke's pseudo r^2...: 0.025714 
Sample size of AE DB......: 2388 
Sample size of model......: 1152 
Missing data %............: 51.75879 

Analysis of MCP1_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]                   Age            Gendermale  
             0.24802               0.26381               0.03239              -0.43504  

Degrees of Freedom: 1195 Total (i.e. Null);  1192 Residual
Null Deviance:      826.5 
Residual Deviance: 804.7    AIC: 812.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5655   0.3738   0.4440   0.5167   0.8433  

Coefficients:
                     Estimate Std. Error z value Pr(>|z|)   
(Intercept)           0.24802    0.69361   0.358  0.72066   
currentDF[, PROTEIN]  0.26381    0.09370   2.816  0.00487 **
Age                   0.03239    0.01011   3.203  0.00136 **
Gendermale           -0.43504    0.21791  -1.996  0.04589 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 826.52  on 1195  degrees of freedom
Residual deviance: 804.68  on 1192  degrees of freedom
AIC: 812.68

Number of Fisher Scoring iterations: 5

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.263812 
Standard error............: 0.093697 
Odds ratio (effect size)..: 1.302 
Lower 95% CI..............: 1.083 
Upper 95% CI..............: 1.564 
Z-value...................: 2.815595 
P-value...................: 0.004868694 
Hosmer and Lemeshow r^2...: 0.026421 
Cox and Snell r^2.........: 0.018093 
Nagelkerke's pseudo r^2...: 0.036261 
Sample size of AE DB......: 2388 
Sample size of model......: 1196 
Missing data %............: 49.91625 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 2

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, and stenosis..

Natural log-transformed data

First we use the natural-log transformed data.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of IL6_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Stroke_history + 
    Peripheral.interv, family = binomial(link = "logit"), data = currentDF)

Coefficients:
      (Intercept)     Stroke_history  Peripheral.interv  
            1.469              1.421             -1.053  

Degrees of Freedom: 413 Total (i.e. Null);  411 Residual
Null Deviance:      388.7 
Residual Deviance: 356.9    AIC: 362.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.6144   0.2692   0.4703   0.6809   1.3036  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.684e+01  1.344e+03   0.013 0.990002    
currentDF[, PROTEIN]      -1.099e-01  1.287e-01  -0.854 0.393205    
Age                        1.544e-02  1.835e-02   0.841 0.400260    
Gendermale                 3.170e-02  3.179e-01   0.100 0.920584    
Hypertension.compositeyes -4.657e-01  4.644e-01  -1.003 0.315945    
DiabetesStatusDiabetes     4.233e-01  3.671e-01   1.153 0.248948    
SmokerCurrentyes          -1.316e-01  2.994e-01  -0.439 0.660340    
Med.Statin.LLDyes         -3.348e-01  3.379e-01  -0.991 0.321782    
Med.all.antiplateletyes   -4.925e-01  5.409e-01  -0.910 0.362567    
GFR_MDRD                   1.059e-02  8.343e-03   1.269 0.204415    
BMI                       -1.891e-02  3.709e-02  -0.510 0.610181    
CAD_history               -1.165e-01  3.027e-01  -0.385 0.700375    
Stroke_history             1.491e+00  3.883e-01   3.841 0.000123 ***
Peripheral.interv         -1.037e+00  3.039e-01  -3.413 0.000642 ***
stenose50-70%             -1.423e+01  1.344e+03  -0.011 0.991550    
stenose70-90%             -1.530e+01  1.344e+03  -0.011 0.990911    
stenose90-99%             -1.502e+01  1.344e+03  -0.011 0.991078    
stenose100% (Occlusion)   -1.352e-01  1.655e+03   0.000 0.999935    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 388.73  on 413  degrees of freedom
Residual deviance: 343.56  on 396  degrees of freedom
AIC: 379.56

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.109894 
Standard error............: 0.128709 
Odds ratio (effect size)..: 0.896 
Lower 95% CI..............: 0.696 
Upper 95% CI..............: 1.153 
Z-value...................: -0.853819 
P-value...................: 0.3932054 
Hosmer and Lemeshow r^2...: 0.116194 
Cox and Snell r^2.........: 0.103361 
Nagelkerke's pseudo r^2...: 0.169731 
Sample size of AE DB......: 2388 
Sample size of model......: 414 
Missing data %............: 82.66332 

Analysis of MCP1_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]        Stroke_history     Peripheral.interv  
              0.3598                0.2606                1.5912               -1.0558  

Degrees of Freedom: 506 Total (i.e. Null);  503 Residual
Null Deviance:      445.4 
Residual Deviance: 401.2    AIC: 409.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8151   0.2361   0.4168   0.6296   1.2837  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.457e+01  1.306e+03   0.011 0.991100    
currentDF[, PROTEIN]       2.898e-01  1.591e-01   1.821 0.068563 .  
Age                        1.607e-02  1.731e-02   0.928 0.353210    
Gendermale                -1.980e-01  3.040e-01  -0.651 0.514833    
Hypertension.compositeyes -6.217e-01  4.585e-01  -1.356 0.175147    
DiabetesStatusDiabetes     3.509e-01  3.435e-01   1.022 0.306992    
SmokerCurrentyes          -4.256e-02  2.845e-01  -0.150 0.881098    
Med.Statin.LLDyes         -9.946e-02  3.268e-01  -0.304 0.760824    
Med.all.antiplateletyes   -7.095e-01  5.344e-01  -1.328 0.184247    
GFR_MDRD                   7.080e-03  7.595e-03   0.932 0.351268    
BMI                        1.454e-02  3.583e-02   0.406 0.684935    
CAD_history               -2.658e-01  2.826e-01  -0.940 0.347088    
Stroke_history             1.601e+00  3.808e-01   4.203 2.63e-05 ***
Peripheral.interv         -1.057e+00  2.946e-01  -3.588 0.000333 ***
stenose50-70%             -1.361e+01  1.306e+03  -0.010 0.991688    
stenose70-90%             -1.514e+01  1.306e+03  -0.012 0.990752    
stenose90-99%             -1.466e+01  1.306e+03  -0.011 0.991046    
stenose100% (Occlusion)   -1.079e-01  1.627e+03   0.000 0.999947    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 445.43  on 506  degrees of freedom
Residual deviance: 386.25  on 489  degrees of freedom
AIC: 422.25

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.289758 
Standard error............: 0.159095 
Odds ratio (effect size)..: 1.336 
Lower 95% CI..............: 0.978 
Upper 95% CI..............: 1.825 
Z-value...................: 1.821291 
P-value...................: 0.0685626 
Hosmer and Lemeshow r^2...: 0.132863 
Cox and Snell r^2.........: 0.110172 
Nagelkerke's pseudo r^2...: 0.188452 
Sample size of AE DB......: 2388 
Sample size of model......: 507 
Missing data %............: 78.76884 

Analysis of IL6_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    Med.all.antiplatelet + GFR_MDRD + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)                      Age               Gendermale  Med.all.antiplateletyes                 GFR_MDRD  
              15.387606                 0.027762                -0.453161                -0.916880                 0.008104  
         Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  
               1.166727                -0.673292               -13.748714               -14.870639               -14.701591  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
              -0.331218               -16.941956                -0.167408  

Degrees of Freedom: 1008 Total (i.e. Null);  996 Residual
Null Deviance:      707.6 
Residual Deviance: 652.1    AIC: 678.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8226   0.2652   0.4017   0.5590   0.9792  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.565e+01  1.027e+03   0.015 0.987838    
currentDF[, PROTEIN]       4.009e-02  7.151e-02   0.561 0.575025    
Age                        3.157e-02  1.296e-02   2.437 0.014805 *  
Gendermale                -4.000e-01  2.443e-01  -1.637 0.101631    
Hypertension.compositeyes -2.988e-01  3.648e-01  -0.819 0.412745    
DiabetesStatusDiabetes    -8.362e-03  2.509e-01  -0.033 0.973407    
SmokerCurrentyes           1.551e-01  2.332e-01   0.665 0.506105    
Med.Statin.LLDyes         -1.772e-01  2.732e-01  -0.649 0.516525    
Med.all.antiplateletyes   -9.046e-01  4.485e-01  -2.017 0.043704 *  
GFR_MDRD                   7.273e-03  5.677e-03   1.281 0.200167    
BMI                        9.375e-04  2.893e-02   0.032 0.974147    
CAD_history               -1.784e-01  2.263e-01  -0.788 0.430588    
Stroke_history             1.108e+00  2.862e-01   3.872 0.000108 ***
Peripheral.interv         -6.278e-01  2.398e-01  -2.618 0.008851 ** 
stenose50-70%             -1.368e+01  1.027e+03  -0.013 0.989373    
stenose70-90%             -1.486e+01  1.027e+03  -0.014 0.988456    
stenose90-99%             -1.469e+01  1.027e+03  -0.014 0.988585    
stenose100% (Occlusion)   -4.360e-01  1.306e+03   0.000 0.999734    
stenose50-99%             -1.691e+01  1.027e+03  -0.016 0.986861    
stenose70-99%             -1.378e-01  1.233e+03   0.000 0.999911    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 707.63  on 1008  degrees of freedom
Residual deviance: 648.67  on  989  degrees of freedom
AIC: 688.67

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.040092 
Standard error............: 0.071508 
Odds ratio (effect size)..: 1.041 
Lower 95% CI..............: 0.905 
Upper 95% CI..............: 1.198 
Z-value...................: 0.560667 
P-value...................: 0.5750247 
Hosmer and Lemeshow r^2...: 0.083324 
Cox and Snell r^2.........: 0.056762 
Nagelkerke's pseudo r^2...: 0.112607 
Sample size of AE DB......: 2388 
Sample size of model......: 1009 
Missing data %............: 57.74707 

Analysis of IL6R_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Med.all.antiplatelet + CAD_history + Stroke_history + 
    Peripheral.interv, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age               Gendermale  Med.all.antiplateletyes  
                0.79707                 -0.18290                  0.02791                 -0.36266                 -0.82533  
            CAD_history           Stroke_history        Peripheral.interv  
               -0.36391                  1.02555                 -0.46692  

Degrees of Freedom: 1011 Total (i.e. Null);  1004 Residual
Null Deviance:      720.7 
Residual Deviance: 675.8    AIC: 691.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8326   0.2694   0.4155   0.5558   1.0215  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.533e+01  1.137e+03   0.013  0.98924    
currentDF[, PROTEIN]      -1.693e-01  9.430e-02  -1.795  0.07258 .  
Age                        3.516e-02  1.291e-02   2.722  0.00648 ** 
Gendermale                -4.114e-01  2.428e-01  -1.695  0.09010 .  
Hypertension.compositeyes -2.233e-01  3.507e-01  -0.637  0.52428    
DiabetesStatusDiabetes    -1.665e-02  2.474e-01  -0.067  0.94635    
SmokerCurrentyes           2.696e-01  2.314e-01   1.165  0.24394    
Med.Statin.LLDyes         -1.900e-01  2.724e-01  -0.698  0.48540    
Med.all.antiplateletyes   -8.694e-01  4.488e-01  -1.937  0.05271 .  
GFR_MDRD                   5.162e-03  5.684e-03   0.908  0.36379    
BMI                       -1.488e-02  2.959e-02  -0.503  0.61510    
CAD_history               -2.771e-01  2.230e-01  -1.243  0.21390    
Stroke_history             1.020e+00  2.714e-01   3.760  0.00017 ***
Peripheral.interv         -5.140e-01  2.410e-01  -2.132  0.03298 *  
stenose50-70%             -1.363e+01  1.137e+03  -0.012  0.99043    
stenose70-90%             -1.479e+01  1.137e+03  -0.013  0.98962    
stenose90-99%             -1.461e+01  1.137e+03  -0.013  0.98974    
stenose100% (Occlusion)   -3.467e-01  1.393e+03   0.000  0.99980    
stenose50-99%             -1.590e+01  1.137e+03  -0.014  0.98884    
stenose70-99%             -2.688e-01  1.327e+03   0.000  0.99984    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 720.70  on 1011  degrees of freedom
Residual deviance: 660.98  on  992  degrees of freedom
AIC: 700.98

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.16932 
Standard error............: 0.094303 
Odds ratio (effect size)..: 0.844 
Lower 95% CI..............: 0.702 
Upper 95% CI..............: 1.016 
Z-value...................: -1.795489 
P-value...................: 0.07257589 
Hosmer and Lemeshow r^2...: 0.08286 
Cox and Snell r^2.........: 0.057302 
Nagelkerke's pseudo r^2...: 0.112486 
Sample size of AE DB......: 2388 
Sample size of model......: 1012 
Missing data %............: 57.62144 

Analysis of MCP1_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Med.all.antiplatelet + GFR_MDRD + Stroke_history + 
    Peripheral.interv + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age               Gendermale  Med.all.antiplateletyes  
              15.469983                 0.168894                 0.031530                -0.520258                -0.924441  
               GFR_MDRD           Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%  
               0.008484                 1.057027                -0.575248               -13.815303               -15.001478  
          stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
             -14.758355                -0.371350               -16.106565                -0.440020  

Degrees of Freedom: 1050 Total (i.e. Null);  1037 Residual
Null Deviance:      742.4 
Residual Deviance: 682.8    AIC: 710.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8977   0.2682   0.4043   0.5523   1.1643  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.554e+01  1.023e+03   0.015 0.987886    
currentDF[, PROTEIN]       1.573e-01  7.463e-02   2.108 0.035040 *  
Age                        3.611e-02  1.269e-02   2.846 0.004422 ** 
Gendermale                -4.591e-01  2.387e-01  -1.923 0.054440 .  
Hypertension.compositeyes -2.078e-01  3.507e-01  -0.592 0.553537    
DiabetesStatusDiabetes     5.715e-02  2.458e-01   0.232 0.816153    
SmokerCurrentyes           2.655e-01  2.290e-01   1.159 0.246334    
Med.Statin.LLDyes         -1.553e-01  2.707e-01  -0.574 0.566039    
Med.all.antiplateletyes   -9.157e-01  4.483e-01  -2.043 0.041096 *  
GFR_MDRD                   7.369e-03  5.556e-03   1.326 0.184788    
BMI                       -4.650e-03  2.793e-02  -0.166 0.867781    
CAD_history               -2.338e-01  2.190e-01  -1.067 0.285776    
Stroke_history             1.007e+00  2.711e-01   3.713 0.000205 ***
Peripheral.interv         -5.514e-01  2.365e-01  -2.332 0.019724 *  
stenose50-70%             -1.370e+01  1.023e+03  -0.013 0.989315    
stenose70-90%             -1.494e+01  1.023e+03  -0.015 0.988351    
stenose90-99%             -1.472e+01  1.023e+03  -0.014 0.988525    
stenose100% (Occlusion)   -4.238e-01  1.309e+03   0.000 0.999742    
stenose50-99%             -1.613e+01  1.023e+03  -0.016 0.987420    
stenose70-99%             -3.740e-01  1.232e+03   0.000 0.999758    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 742.44  on 1050  degrees of freedom
Residual deviance: 678.70  on 1031  degrees of freedom
AIC: 718.7

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.157312 
Standard error............: 0.07463 
Odds ratio (effect size)..: 1.17 
Lower 95% CI..............: 1.011 
Upper 95% CI..............: 1.355 
Z-value...................: 2.107901 
P-value...................: 0.03503953 
Hosmer and Lemeshow r^2...: 0.085846 
Cox and Snell r^2.........: 0.058841 
Nagelkerke's pseudo r^2...: 0.116151 
Sample size of AE DB......: 2388 
Sample size of model......: 1051 
Missing data %............: 55.98828 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of IL6_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Stroke_history + 
    Peripheral.interv, family = binomial(link = "logit"), data = currentDF)

Coefficients:
      (Intercept)     Stroke_history  Peripheral.interv  
           1.4460             1.5481            -0.9696  

Degrees of Freedom: 480 Total (i.e. Null);  478 Residual
Null Deviance:      448.7 
Residual Deviance: 411  AIC: 417

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8207   0.2472   0.4518   0.6797   1.2683  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.547e+01  1.350e+03   0.011 0.990858    
currentDF[, PROTEIN]      -1.637e-01  1.341e-01  -1.221 0.222219    
Age                        2.709e-02  1.676e-02   1.616 0.106099    
Gendermale                -1.779e-01  2.976e-01  -0.598 0.549922    
Hypertension.compositeyes -5.697e-01  4.359e-01  -1.307 0.191211    
DiabetesStatusDiabetes     3.403e-01  3.345e-01   1.017 0.308976    
SmokerCurrentyes           1.065e-01  2.813e-01   0.379 0.704984    
Med.Statin.LLDyes         -1.716e-01  3.130e-01  -0.548 0.583524    
Med.all.antiplateletyes   -6.819e-01  5.289e-01  -1.289 0.197347    
GFR_MDRD                   1.046e-02  7.628e-03   1.371 0.170375    
BMI                       -8.104e-03  3.455e-02  -0.235 0.814531    
CAD_history               -8.513e-02  2.809e-01  -0.303 0.761814    
Stroke_history             1.578e+00  3.808e-01   4.143 3.42e-05 ***
Peripheral.interv         -9.874e-01  2.864e-01  -3.448 0.000564 ***
stenose50-70%             -1.393e+01  1.350e+03  -0.010 0.991766    
stenose70-90%             -1.532e+01  1.350e+03  -0.011 0.990946    
stenose90-99%             -1.486e+01  1.350e+03  -0.011 0.991218    
stenose100% (Occlusion)   -1.371e-01  1.610e+03   0.000 0.999932    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 448.65  on 480  degrees of freedom
Residual deviance: 393.24  on 463  degrees of freedom
AIC: 429.24

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.163678 
Standard error............: 0.134091 
Odds ratio (effect size)..: 0.849 
Lower 95% CI..............: 0.653 
Upper 95% CI..............: 1.104 
Z-value...................: -1.220648 
P-value...................: 0.2222193 
Hosmer and Lemeshow r^2...: 0.123507 
Cox and Snell r^2.........: 0.108813 
Nagelkerke's pseudo r^2...: 0.179402 
Sample size of AE DB......: 2388 
Sample size of model......: 481 
Missing data %............: 79.85762 

Analysis of MCP1_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Med.all.antiplatelet + Stroke_history + Peripheral.interv, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]  Med.all.antiplateletyes           Stroke_history        Peripheral.interv  
                 2.1710                   0.3020                  -0.7129                   1.6297                  -0.9739  

Degrees of Freedom: 513 Total (i.e. Null);  509 Residual
Null Deviance:      457.8 
Residual Deviance: 409  AIC: 419

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8828   0.2333   0.4237   0.6299   1.2824  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.496e+01  1.305e+03   0.011 0.990854    
currentDF[, PROTEIN]       3.464e-01  1.367e-01   2.534 0.011287 *  
Age                        2.580e-02  1.666e-02   1.549 0.121368    
Gendermale                -2.685e-01  2.997e-01  -0.896 0.370282    
Hypertension.compositeyes -5.009e-01  4.399e-01  -1.139 0.254855    
DiabetesStatusDiabetes     2.651e-01  3.307e-01   0.802 0.422680    
SmokerCurrentyes           7.686e-02  2.804e-01   0.274 0.783994    
Med.Statin.LLDyes         -4.258e-02  3.194e-01  -0.133 0.893950    
Med.all.antiplateletyes   -8.355e-01  5.289e-01  -1.580 0.114152    
GFR_MDRD                   8.463e-03  7.526e-03   1.125 0.260766    
BMI                        1.930e-02  3.529e-02   0.547 0.584325    
CAD_history               -2.171e-01  2.814e-01  -0.771 0.440413    
Stroke_history             1.598e+00  3.795e-01   4.210 2.55e-05 ***
Peripheral.interv         -9.893e-01  2.939e-01  -3.367 0.000761 ***
stenose50-70%             -1.353e+01  1.305e+03  -0.010 0.991724    
stenose70-90%             -1.511e+01  1.305e+03  -0.012 0.990760    
stenose90-99%             -1.466e+01  1.305e+03  -0.011 0.991034    
stenose100% (Occlusion)   -1.429e-01  1.629e+03   0.000 0.999930    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 457.77  on 513  degrees of freedom
Residual deviance: 395.26  on 496  degrees of freedom
AIC: 431.26

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.346429 
Standard error............: 0.136729 
Odds ratio (effect size)..: 1.414 
Lower 95% CI..............: 1.082 
Upper 95% CI..............: 1.849 
Z-value...................: 2.533683 
P-value...................: 0.01128706 
Hosmer and Lemeshow r^2...: 0.136557 
Cox and Snell r^2.........: 0.114514 
Nagelkerke's pseudo r^2...: 0.194226 
Sample size of AE DB......: 2388 
Sample size of model......: 514 
Missing data %............: 78.47571 

Analysis of IL6_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    Med.all.antiplatelet + GFR_MDRD + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)                      Age               Gendermale  Med.all.antiplateletyes                 GFR_MDRD  
              15.387606                 0.027762                -0.453161                -0.916880                 0.008104  
         Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  
               1.166727                -0.673292               -13.748714               -14.870639               -14.701591  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
              -0.331218               -16.941956                -0.167408  

Degrees of Freedom: 1008 Total (i.e. Null);  996 Residual
Null Deviance:      707.6 
Residual Deviance: 652.1    AIC: 678.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8197   0.2654   0.4021   0.5591   0.9812  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.553e+01  1.027e+03   0.015 0.987936    
currentDF[, PROTEIN]       6.163e-02  1.053e-01   0.585 0.558336    
Age                        3.155e-02  1.295e-02   2.436 0.014857 *  
Gendermale                -4.004e-01  2.443e-01  -1.639 0.101287    
Hypertension.compositeyes -2.983e-01  3.648e-01  -0.818 0.413500    
DiabetesStatusDiabetes    -8.036e-03  2.508e-01  -0.032 0.974442    
SmokerCurrentyes           1.551e-01  2.332e-01   0.665 0.505947    
Med.Statin.LLDyes         -1.778e-01  2.733e-01  -0.651 0.515164    
Med.all.antiplateletyes   -9.050e-01  4.485e-01  -2.018 0.043614 *  
GFR_MDRD                   7.277e-03  5.677e-03   1.282 0.199907    
BMI                        9.863e-04  2.892e-02   0.034 0.972798    
CAD_history               -1.788e-01  2.262e-01  -0.790 0.429472    
Stroke_history             1.108e+00  2.862e-01   3.870 0.000109 ***
Peripheral.interv         -6.265e-01  2.400e-01  -2.611 0.009031 ** 
stenose50-70%             -1.368e+01  1.027e+03  -0.013 0.989371    
stenose70-90%             -1.486e+01  1.027e+03  -0.014 0.988453    
stenose90-99%             -1.469e+01  1.027e+03  -0.014 0.988582    
stenose100% (Occlusion)   -4.303e-01  1.306e+03   0.000 0.999737    
stenose50-99%             -1.691e+01  1.027e+03  -0.016 0.986859    
stenose70-99%             -1.372e-01  1.233e+03   0.000 0.999911    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 707.63  on 1008  degrees of freedom
Residual deviance: 648.64  on  989  degrees of freedom
AIC: 688.64

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.061634 
Standard error............: 0.1053 
Odds ratio (effect size)..: 1.064 
Lower 95% CI..............: 0.865 
Upper 95% CI..............: 1.307 
Z-value...................: 0.585315 
P-value...................: 0.5583357 
Hosmer and Lemeshow r^2...: 0.083364 
Cox and Snell r^2.........: 0.056789 
Nagelkerke's pseudo r^2...: 0.11266 
Sample size of AE DB......: 2388 
Sample size of model......: 1009 
Missing data %............: 57.74707 

Analysis of IL6R_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Med.all.antiplatelet + CAD_history + Stroke_history + 
    Peripheral.interv, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age               Gendermale  Med.all.antiplateletyes  
                1.16545                 -0.16682                  0.02796                 -0.35834                 -0.82910  
            CAD_history           Stroke_history        Peripheral.interv  
               -0.35351                  1.02217                 -0.47073  

Degrees of Freedom: 1012 Total (i.e. Null);  1005 Residual
Null Deviance:      720.9 
Residual Deviance: 677.2    AIC: 693.2

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8361   0.2711   0.4157   0.5602   1.0121  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.566e+01  1.140e+03   0.014 0.989035    
currentDF[, PROTEIN]      -1.527e-01  1.016e-01  -1.503 0.132769    
Age                        3.521e-02  1.291e-02   2.728 0.006368 ** 
Gendermale                -4.077e-01  2.427e-01  -1.680 0.092969 .  
Hypertension.compositeyes -2.212e-01  3.506e-01  -0.631 0.528045    
DiabetesStatusDiabetes    -9.310e-03  2.471e-01  -0.038 0.969943    
SmokerCurrentyes           2.704e-01  2.313e-01   1.169 0.242377    
Med.Statin.LLDyes         -1.877e-01  2.725e-01  -0.689 0.491028    
Med.all.antiplateletyes   -8.710e-01  4.486e-01  -1.942 0.052179 .  
GFR_MDRD                   5.154e-03  5.688e-03   0.906 0.364834    
BMI                       -1.404e-02  2.963e-02  -0.474 0.635537    
CAD_history               -2.680e-01  2.228e-01  -1.203 0.228970    
Stroke_history             1.017e+00  2.712e-01   3.751 0.000176 ***
Peripheral.interv         -5.189e-01  2.410e-01  -2.153 0.031324 *  
stenose50-70%             -1.365e+01  1.140e+03  -0.012 0.990444    
stenose70-90%             -1.482e+01  1.140e+03  -0.013 0.989628    
stenose90-99%             -1.464e+01  1.140e+03  -0.013 0.989752    
stenose100% (Occlusion)   -3.777e-01  1.396e+03   0.000 0.999784    
stenose50-99%             -1.595e+01  1.140e+03  -0.014 0.988836    
stenose70-99%             -2.526e-01  1.330e+03   0.000 0.999848    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 720.94  on 1012  degrees of freedom
Residual deviance: 662.33  on  993  degrees of freedom
AIC: 702.33

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.152701 
Standard error............: 0.101579 
Odds ratio (effect size)..: 0.858 
Lower 95% CI..............: 0.703 
Upper 95% CI..............: 1.047 
Z-value...................: -1.503271 
P-value...................: 0.1327691 
Hosmer and Lemeshow r^2...: 0.081298 
Cox and Snell r^2.........: 0.056217 
Nagelkerke's pseudo r^2...: 0.110406 
Sample size of AE DB......: 2388 
Sample size of model......: 1013 
Missing data %............: 57.57956 

Analysis of MCP1_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Med.all.antiplatelet + GFR_MDRD + Stroke_history + 
    Peripheral.interv + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age               Gendermale  Med.all.antiplateletyes  
              15.272239                 0.246808                 0.031733                -0.519803                -0.925714  
               GFR_MDRD           Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%  
               0.008448                 1.056452                -0.572007               -13.826519               -15.017030  
          stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
             -14.770785                -0.381146               -16.127189                -0.475975  

Degrees of Freedom: 1050 Total (i.e. Null);  1037 Residual
Null Deviance:      742.4 
Residual Deviance: 681.8    AIC: 709.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9026   0.2665   0.4041   0.5510   1.1581  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.535e+01  1.022e+03   0.015 0.988014    
currentDF[, PROTEIN]       2.327e-01  1.012e-01   2.299 0.021489 *  
Age                        3.628e-02  1.270e-02   2.857 0.004280 ** 
Gendermale                -4.583e-01  2.387e-01  -1.920 0.054877 .  
Hypertension.compositeyes -2.011e-01  3.509e-01  -0.573 0.566720    
DiabetesStatusDiabetes     5.331e-02  2.459e-01   0.217 0.828362    
SmokerCurrentyes           2.658e-01  2.291e-01   1.160 0.245990    
Med.Statin.LLDyes         -1.571e-01  2.708e-01  -0.580 0.561715    
Med.all.antiplateletyes   -9.184e-01  4.485e-01  -2.048 0.040595 *  
GFR_MDRD                   7.330e-03  5.560e-03   1.318 0.187367    
BMI                       -4.704e-03  2.790e-02  -0.169 0.866132    
CAD_history               -2.356e-01  2.191e-01  -1.076 0.282037    
Stroke_history             1.006e+00  2.713e-01   3.707 0.000209 ***
Peripheral.interv         -5.483e-01  2.366e-01  -2.317 0.020485 *  
stenose50-70%             -1.371e+01  1.022e+03  -0.013 0.989296    
stenose70-90%             -1.495e+01  1.022e+03  -0.015 0.988326    
stenose90-99%             -1.473e+01  1.022e+03  -0.014 0.988502    
stenose100% (Occlusion)   -4.325e-01  1.308e+03   0.000 0.999736    
stenose50-99%             -1.616e+01  1.022e+03  -0.016 0.987388    
stenose70-99%             -4.053e-01  1.231e+03   0.000 0.999737    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 742.44  on 1050  degrees of freedom
Residual deviance: 677.73  on 1031  degrees of freedom
AIC: 717.73

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.232718 
Standard error............: 0.101213 
Odds ratio (effect size)..: 1.262 
Lower 95% CI..............: 1.035 
Upper 95% CI..............: 1.539 
Z-value...................: 2.299283 
P-value...................: 0.02148888 
Hosmer and Lemeshow r^2...: 0.087159 
Cox and Snell r^2.........: 0.059713 
Nagelkerke's pseudo r^2...: 0.117873 
Sample size of AE DB......: 2388 
Sample size of model......: 1051 
Missing data %............: 55.98828 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 3

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, stenosis., and LDL.

Natural log-transformed data

First we use the natural-log transformed data.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of IL6_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Med.Statin.LLD + 
    Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
      (Intercept)  Med.Statin.LLDyes     Stroke_history  Peripheral.interv  
           1.8237            -0.6408             1.4565            -1.0636  

Degrees of Freedom: 286 Total (i.e. Null);  283 Residual
Null Deviance:      283.3 
Residual Deviance: 255.8    AIC: 263.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.6583   0.2308   0.4365   0.7133   1.3582  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.743e+01  2.400e+03   0.007 0.994206    
currentDF[, PROTEIN]      -9.886e-02  1.530e-01  -0.646 0.518133    
Age                       -1.025e-02  2.244e-02  -0.457 0.647926    
Gendermale                 1.919e-01  3.766e-01   0.510 0.610376    
Hypertension.compositeyes -9.182e-01  6.105e-01  -1.504 0.132598    
DiabetesStatusDiabetes     4.874e-01  4.627e-01   1.053 0.292172    
SmokerCurrentyes          -2.154e-01  3.505e-01  -0.614 0.538905    
Med.Statin.LLDyes         -6.461e-01  4.158e-01  -1.554 0.120274    
Med.all.antiplateletyes   -2.635e-01  7.234e-01  -0.364 0.715643    
GFR_MDRD                   1.023e-02  9.787e-03   1.045 0.295822    
BMI                        2.828e-02  4.613e-02   0.613 0.539897    
CAD_history                5.117e-02  3.611e-01   0.142 0.887320    
Stroke_history             1.660e+00  4.647e-01   3.572 0.000354 ***
Peripheral.interv         -1.068e+00  3.563e-01  -2.997 0.002728 ** 
stenose50-70%             -1.521e+01  2.400e+03  -0.006 0.994943    
stenose70-90%             -1.561e+01  2.400e+03  -0.007 0.994810    
stenose90-99%             -1.500e+01  2.400e+03  -0.006 0.995013    
stenose100% (Occlusion)   -6.262e-01  2.644e+03   0.000 0.999811    
LDL_final                  4.615e-02  1.772e-01   0.260 0.794515    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 283.31  on 286  degrees of freedom
Residual deviance: 245.47  on 268  degrees of freedom
AIC: 283.47

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.09886 
Standard error............: 0.15298 
Odds ratio (effect size)..: 0.906 
Lower 95% CI..............: 0.671 
Upper 95% CI..............: 1.223 
Z-value...................: -0.646226 
P-value...................: 0.518133 
Hosmer and Lemeshow r^2...: 0.133551 
Cox and Snell r^2.........: 0.123512 
Nagelkerke's pseudo r^2...: 0.196878 
Sample size of AE DB......: 2388 
Sample size of model......: 287 
Missing data %............: 87.98158 

Analysis of MCP1_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    Med.Statin.LLD + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes          Med.Statin.LLDyes             Stroke_history          Peripheral.interv  
                   2.7182                    -0.8944                    -0.5833                     1.5625                    -1.0394  

Degrees of Freedom: 356 Total (i.e. Null);  352 Residual
Null Deviance:      326.5 
Residual Deviance: 290.8    AIC: 300.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8603   0.2234   0.4378   0.6500   1.4250  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.595e+01  2.400e+03   0.007  0.99470    
currentDF[, PROTEIN]       1.221e-01  1.860e-01   0.657  0.51131    
Age                       -9.181e-03  2.074e-02  -0.443  0.65799    
Gendermale                 8.686e-02  3.562e-01   0.244  0.80736    
Hypertension.compositeyes -1.162e+00  5.939e-01  -1.956  0.05044 .  
DiabetesStatusDiabetes     4.687e-01  4.264e-01   1.099  0.27171    
SmokerCurrentyes          -1.667e-01  3.273e-01  -0.509  0.61066    
Med.Statin.LLDyes         -5.187e-01  4.069e-01  -1.275  0.20240    
Med.all.antiplateletyes   -3.246e-01  7.023e-01  -0.462  0.64397    
GFR_MDRD                   3.197e-03  9.074e-03   0.352  0.72460    
BMI                        5.208e-02  4.258e-02   1.223  0.22133    
CAD_history                4.633e-02  3.387e-01   0.137  0.89120    
Stroke_history             1.776e+00  4.539e-01   3.913 9.11e-05 ***
Peripheral.interv         -1.059e+00  3.409e-01  -3.107  0.00189 ** 
stenose50-70%             -1.395e+01  2.400e+03  -0.006  0.99536    
stenose70-90%             -1.508e+01  2.400e+03  -0.006  0.99499    
stenose90-99%             -1.439e+01  2.400e+03  -0.006  0.99522    
stenose100% (Occlusion)   -1.415e-01  2.638e+03   0.000  0.99996    
LDL_final                  7.818e-02  1.579e-01   0.495  0.62044    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 326.48  on 356  degrees of freedom
Residual deviance: 280.97  on 338  degrees of freedom
AIC: 318.97

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.122134 
Standard error............: 0.185953 
Odds ratio (effect size)..: 1.13 
Lower 95% CI..............: 0.785 
Upper 95% CI..............: 1.627 
Z-value...................: 0.656804 
P-value...................: 0.5113069 
Hosmer and Lemeshow r^2...: 0.139416 
Cox and Snell r^2.........: 0.119706 
Nagelkerke's pseudo r^2...: 0.199745 
Sample size of AE DB......: 2388 
Sample size of model......: 357 
Missing data %............: 85.05025 

Analysis of IL6_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Med.Statin.LLD + 
    Med.all.antiplatelet + Stroke_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)        Med.Statin.LLDyes  Med.all.antiplateletyes           Stroke_history  
                 3.1771                  -0.6599                  -0.9416                   1.5973  

Degrees of Freedom: 627 Total (i.e. Null);  624 Residual
Null Deviance:      430.7 
Residual Deviance: 402.5    AIC: 410.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9013   0.2226   0.3631   0.5804   1.0099  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.624e+01  1.229e+03   0.013 0.989456    
currentDF[, PROTEIN]       6.654e-02  9.000e-02   0.739 0.459706    
Age                        2.066e-02  1.681e-02   1.229 0.218929    
Gendermale                -2.292e-01  3.088e-01  -0.742 0.457880    
Hypertension.compositeyes -4.168e-01  5.055e-01  -0.825 0.409591    
DiabetesStatusDiabetes     1.173e-01  3.342e-01   0.351 0.725677    
SmokerCurrentyes           1.632e-01  2.972e-01   0.549 0.582939    
Med.Statin.LLDyes         -5.393e-01  3.809e-01  -1.416 0.156827    
Med.all.antiplateletyes   -8.736e-01  6.272e-01  -1.393 0.163638    
GFR_MDRD                   9.608e-03  7.419e-03   1.295 0.195315    
BMI                       -9.234e-03  3.595e-02  -0.257 0.797294    
CAD_history                1.169e-01  2.991e-01   0.391 0.695979    
Stroke_history             1.520e+00  4.175e-01   3.641 0.000272 ***
Peripheral.interv         -3.896e-01  3.152e-01  -1.236 0.216371    
stenose50-70%             -1.345e+01  1.229e+03  -0.011 0.991264    
stenose70-90%             -1.472e+01  1.229e+03  -0.012 0.990444    
stenose90-99%             -1.480e+01  1.229e+03  -0.012 0.990388    
stenose100% (Occlusion)   -3.152e-01  1.488e+03   0.000 0.999831    
stenose70-99%             -6.828e-01  1.680e+03   0.000 0.999676    
LDL_final                  1.413e-01  1.428e-01   0.990 0.322215    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 430.69  on 627  degrees of freedom
Residual deviance: 389.70  on 608  degrees of freedom
AIC: 429.7

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.066541 
Standard error............: 0.090002 
Odds ratio (effect size)..: 1.069 
Lower 95% CI..............: 0.896 
Upper 95% CI..............: 1.275 
Z-value...................: 0.739331 
P-value...................: 0.4597058 
Hosmer and Lemeshow r^2...: 0.095178 
Cox and Snell r^2.........: 0.063189 
Nagelkerke's pseudo r^2...: 0.127316 
Sample size of AE DB......: 2388 
Sample size of model......: 628 
Missing data %............: 73.70184 

Analysis of IL6R_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Med.Statin.LLD + Med.all.antiplatelet + Stroke_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]        Med.Statin.LLDyes  Med.all.antiplateletyes           Stroke_history  
                 2.7049                  -0.2566                  -0.7107                  -0.9239                   1.5983  

Degrees of Freedom: 621 Total (i.e. Null);  617 Residual
Null Deviance:      437.6 
Residual Deviance: 404.5    AIC: 414.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8997   0.2272   0.3710   0.5857   1.0333  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.636e+01  1.460e+03   0.011 0.991063    
currentDF[, PROTEIN]      -2.183e-01  1.310e-01  -1.667 0.095557 .  
Age                        2.062e-02  1.696e-02   1.216 0.223987    
Gendermale                -2.038e-01  3.036e-01  -0.671 0.502095    
Hypertension.compositeyes -2.825e-01  4.701e-01  -0.601 0.547898    
DiabetesStatusDiabetes     1.002e-01  3.345e-01   0.300 0.764523    
SmokerCurrentyes           2.855e-01  2.958e-01   0.965 0.334510    
Med.Statin.LLDyes         -5.857e-01  3.858e-01  -1.518 0.128986    
Med.all.antiplateletyes   -8.778e-01  6.298e-01  -1.394 0.163379    
GFR_MDRD                   7.729e-03  7.508e-03   1.029 0.303282    
BMI                       -2.886e-02  3.764e-02  -0.767 0.443288    
CAD_history                3.983e-02  2.975e-01   0.134 0.893487    
Stroke_history             1.563e+00  4.159e-01   3.757 0.000172 ***
Peripheral.interv         -3.196e-01  3.172e-01  -1.007 0.313790    
stenose50-70%             -1.362e+01  1.460e+03  -0.009 0.992555    
stenose70-90%             -1.487e+01  1.460e+03  -0.010 0.991872    
stenose90-99%             -1.495e+01  1.460e+03  -0.010 0.991831    
stenose100% (Occlusion)   -5.480e-01  1.681e+03   0.000 0.999740    
stenose70-99%             -8.238e-01  1.824e+03   0.000 0.999640    
LDL_final                  1.095e-01  1.432e-01   0.765 0.444488    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 437.63  on 621  degrees of freedom
Residual deviance: 393.77  on 602  degrees of freedom
AIC: 433.77

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.218277 
Standard error............: 0.130957 
Odds ratio (effect size)..: 0.804 
Lower 95% CI..............: 0.622 
Upper 95% CI..............: 1.039 
Z-value...................: -1.666785 
P-value...................: 0.09555717 
Hosmer and Lemeshow r^2...: 0.100216 
Cox and Snell r^2.........: 0.068082 
Nagelkerke's pseudo r^2...: 0.134765 
Sample size of AE DB......: 2388 
Sample size of model......: 622 
Missing data %............: 73.9531 

Analysis of MCP1_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Med.Statin.LLD + 
    Med.all.antiplatelet + Stroke_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)        Med.Statin.LLDyes  Med.all.antiplateletyes           Stroke_history  
                 3.2384                  -0.6894                  -0.9942                   1.6182  

Degrees of Freedom: 643 Total (i.e. Null);  640 Residual
Null Deviance:      447 
Residual Deviance: 416.9    AIC: 424.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0012   0.2287   0.3669   0.5798   1.0121  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.613e+01  1.224e+03   0.013 0.989483    
currentDF[, PROTEIN]       8.739e-02  9.435e-02   0.926 0.354313    
Age                        2.107e-02  1.664e-02   1.266 0.205401    
Gendermale                -2.293e-01  3.001e-01  -0.764 0.444929    
Hypertension.compositeyes -2.522e-01  4.705e-01  -0.536 0.591933    
DiabetesStatusDiabetes     1.980e-01  3.319e-01   0.597 0.550823    
SmokerCurrentyes           2.748e-01  2.931e-01   0.938 0.348465    
Med.Statin.LLDyes         -5.582e-01  3.798e-01  -1.470 0.141601    
Med.all.antiplateletyes   -9.239e-01  6.261e-01  -1.476 0.140042    
GFR_MDRD                   1.026e-02  7.344e-03   1.398 0.162163    
BMI                       -1.469e-02  3.490e-02  -0.421 0.673791    
CAD_history                3.673e-02  2.893e-01   0.127 0.898982    
Stroke_history             1.540e+00  4.161e-01   3.702 0.000214 ***
Peripheral.interv         -3.612e-01  3.123e-01  -1.157 0.247446    
stenose50-70%             -1.341e+01  1.224e+03  -0.011 0.991254    
stenose70-90%             -1.472e+01  1.224e+03  -0.012 0.990403    
stenose90-99%             -1.478e+01  1.224e+03  -0.012 0.990363    
stenose100% (Occlusion)   -3.058e-01  1.487e+03   0.000 0.999836    
stenose70-99%             -7.269e-01  1.674e+03   0.000 0.999654    
LDL_final                  1.158e-01  1.394e-01   0.831 0.406028    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 446.98  on 643  degrees of freedom
Residual deviance: 403.91  on 624  degrees of freedom
AIC: 443.91

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.08739 
Standard error............: 0.094348 
Odds ratio (effect size)..: 1.091 
Lower 95% CI..............: 0.907 
Upper 95% CI..............: 1.313 
Z-value...................: 0.926256 
P-value...................: 0.3543129 
Hosmer and Lemeshow r^2...: 0.096355 
Cox and Snell r^2.........: 0.06469 
Nagelkerke's pseudo r^2...: 0.129261 
Sample size of AE DB......: 2388 
Sample size of model......: 644 
Missing data %............: 73.03183 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.Symptoms.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of IL6_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    Med.Statin.LLD + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes          Med.Statin.LLDyes             Stroke_history          Peripheral.interv  
                   2.3019                    -0.6834                    -0.4871                     1.5512                    -0.8887  

Degrees of Freedom: 340 Total (i.e. Null);  336 Residual
Null Deviance:      332.2 
Residual Deviance: 300.6    AIC: 310.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.7064   0.2184   0.4642   0.6939   1.3335  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.503e+01  2.400e+03   0.006 0.995001    
currentDF[, PROTEIN]      -1.678e-01  1.551e-01  -1.082 0.279290    
Age                        8.507e-03  1.982e-02   0.429 0.667733    
Gendermale                -1.123e-02  3.495e-01  -0.032 0.974361    
Hypertension.compositeyes -9.387e-01  5.484e-01  -1.712 0.086960 .  
DiabetesStatusDiabetes     2.877e-01  4.062e-01   0.708 0.478734    
SmokerCurrentyes           6.318e-02  3.245e-01   0.195 0.845601    
Med.Statin.LLDyes         -3.893e-01  3.852e-01  -1.011 0.312188    
Med.all.antiplateletyes   -4.096e-01  7.039e-01  -0.582 0.560617    
GFR_MDRD                   9.846e-03  9.026e-03   1.091 0.275351    
BMI                        4.511e-02  4.280e-02   1.054 0.291860    
CAD_history                1.774e-01  3.336e-01   0.532 0.594950    
Stroke_history             1.716e+00  4.478e-01   3.833 0.000127 ***
Peripheral.interv         -9.306e-01  3.312e-01  -2.809 0.004964 ** 
stenose50-70%             -1.474e+01  2.400e+03  -0.006 0.995098    
stenose70-90%             -1.563e+01  2.400e+03  -0.007 0.994803    
stenose90-99%             -1.491e+01  2.400e+03  -0.006 0.995042    
stenose100% (Occlusion)   -4.936e-01  2.598e+03   0.000 0.999848    
LDL_final                  1.420e-01  1.596e-01   0.890 0.373659    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 332.21  on 340  degrees of freedom
Residual deviance: 288.59  on 322  degrees of freedom
AIC: 326.59

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.16776 
Standard error............: 0.155058 
Odds ratio (effect size)..: 0.846 
Lower 95% CI..............: 0.624 
Upper 95% CI..............: 1.146 
Z-value...................: -1.081915 
P-value...................: 0.2792904 
Hosmer and Lemeshow r^2...: 0.131299 
Cox and Snell r^2.........: 0.120072 
Nagelkerke's pseudo r^2...: 0.192882 
Sample size of AE DB......: 2388 
Sample size of model......: 341 
Missing data %............: 85.72027 

Analysis of MCP1_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Med.Statin.LLD + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
         (Intercept)  currentDF[, PROTEIN]     Med.Statin.LLDyes        Stroke_history     Peripheral.interv  
              1.8016                0.2201               -0.5266                1.5858               -0.9498  

Degrees of Freedom: 361 Total (i.e. Null);  357 Residual
Null Deviance:      337.7 
Residual Deviance: 302.5    AIC: 312.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9148   0.2219   0.4302   0.6571   1.4101  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.492e+01  2.400e+03   0.006   0.9950    
currentDF[, PROTEIN]       2.476e-01  1.571e-01   1.576   0.1149    
Age                        5.395e-03  1.958e-02   0.276   0.7829    
Gendermale                 2.885e-03  3.485e-01   0.008   0.9934    
Hypertension.compositeyes -8.618e-01  5.437e-01  -1.585   0.1130    
DiabetesStatusDiabetes     2.990e-01  4.035e-01   0.741   0.4587    
SmokerCurrentyes          -1.908e-02  3.211e-01  -0.059   0.9526    
Med.Statin.LLDyes         -3.412e-01  3.877e-01  -0.880   0.3788    
Med.all.antiplateletyes   -6.108e-01  6.969e-01  -0.876   0.3808    
GFR_MDRD                   4.538e-03  8.951e-03   0.507   0.6122    
BMI                        5.397e-02  4.123e-02   1.309   0.1906    
CAD_history                7.558e-02  3.358e-01   0.225   0.8219    
Stroke_history             1.738e+00  4.468e-01   3.890   0.0001 ***
Peripheral.interv         -9.657e-01  3.364e-01  -2.870   0.0041 ** 
stenose50-70%             -1.369e+01  2.400e+03  -0.006   0.9954    
stenose70-90%             -1.483e+01  2.400e+03  -0.006   0.9951    
stenose90-99%             -1.423e+01  2.400e+03  -0.006   0.9953    
stenose100% (Occlusion)    5.240e-02  2.639e+03   0.000   1.0000    
LDL_final                  1.165e-01  1.571e-01   0.742   0.4584    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 337.75  on 361  degrees of freedom
Residual deviance: 291.85  on 343  degrees of freedom
AIC: 329.85

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.24763 
Standard error............: 0.157079 
Odds ratio (effect size)..: 1.281 
Lower 95% CI..............: 0.942 
Upper 95% CI..............: 1.743 
Z-value...................: 1.576468 
P-value...................: 0.1149179 
Hosmer and Lemeshow r^2...: 0.135895 
Cox and Snell r^2.........: 0.119082 
Nagelkerke's pseudo r^2...: 0.196301 
Sample size of AE DB......: 2388 
Sample size of model......: 362 
Missing data %............: 84.84087 

Analysis of IL6_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Med.Statin.LLD + 
    Med.all.antiplatelet + Stroke_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)        Med.Statin.LLDyes  Med.all.antiplateletyes           Stroke_history  
                 3.1771                  -0.6599                  -0.9416                   1.5973  

Degrees of Freedom: 627 Total (i.e. Null);  624 Residual
Null Deviance:      430.7 
Residual Deviance: 402.5    AIC: 410.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8925   0.2219   0.3628   0.5798   1.0169  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.601e+01  1.228e+03   0.013 0.989598    
currentDF[, PROTEIN]       1.091e-01  1.344e-01   0.812 0.416921    
Age                        2.057e-02  1.680e-02   1.224 0.220796    
Gendermale                -2.285e-01  3.088e-01  -0.740 0.459267    
Hypertension.compositeyes -4.162e-01  5.055e-01  -0.823 0.410251    
DiabetesStatusDiabetes     1.200e-01  3.342e-01   0.359 0.719686    
SmokerCurrentyes           1.622e-01  2.972e-01   0.546 0.585130    
Med.Statin.LLDyes         -5.403e-01  3.809e-01  -1.418 0.156084    
Med.all.antiplateletyes   -8.754e-01  6.273e-01  -1.395 0.162898    
GFR_MDRD                   9.640e-03  7.422e-03   1.299 0.193998    
BMI                       -8.759e-03  3.597e-02  -0.244 0.807588    
CAD_history                1.165e-01  2.990e-01   0.390 0.696832    
Stroke_history             1.518e+00  4.174e-01   3.637 0.000275 ***
Peripheral.interv         -3.876e-01  3.153e-01  -1.230 0.218860    
stenose50-70%             -1.345e+01  1.228e+03  -0.011 0.991262    
stenose70-90%             -1.472e+01  1.228e+03  -0.012 0.990440    
stenose90-99%             -1.480e+01  1.228e+03  -0.012 0.990386    
stenose100% (Occlusion)   -3.087e-01  1.487e+03   0.000 0.999834    
stenose70-99%             -6.794e-01  1.680e+03   0.000 0.999677    
LDL_final                  1.423e-01  1.428e-01   0.996 0.319054    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 430.69  on 627  degrees of freedom
Residual deviance: 389.58  on 608  degrees of freedom
AIC: 429.58

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.10907 
Standard error............: 0.13436 
Odds ratio (effect size)..: 1.115 
Lower 95% CI..............: 0.857 
Upper 95% CI..............: 1.451 
Z-value...................: 0.811775 
P-value...................: 0.4169206 
Hosmer and Lemeshow r^2...: 0.095441 
Cox and Snell r^2.........: 0.063358 
Nagelkerke's pseudo r^2...: 0.127657 
Sample size of AE DB......: 2388 
Sample size of model......: 628 
Missing data %............: 73.70184 

Analysis of IL6R_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Med.Statin.LLD + Med.all.antiplatelet + Stroke_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]        Med.Statin.LLDyes  Med.all.antiplateletyes           Stroke_history  
                 3.2331                  -0.2533                  -0.7112                  -0.9302                   1.5950  

Degrees of Freedom: 621 Total (i.e. Null);  617 Residual
Null Deviance:      437.6 
Residual Deviance: 405  AIC: 415

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8977   0.2263   0.3772   0.5824   1.0301  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.675e+01  1.461e+03   0.011 0.990849    
currentDF[, PROTEIN]      -2.135e-01  1.341e-01  -1.591 0.111538    
Age                        2.067e-02  1.695e-02   1.220 0.222595    
Gendermale                -1.975e-01  3.036e-01  -0.651 0.515298    
Hypertension.compositeyes -2.786e-01  4.702e-01  -0.593 0.553454    
DiabetesStatusDiabetes     1.106e-01  3.342e-01   0.331 0.740614    
SmokerCurrentyes           2.920e-01  2.957e-01   0.988 0.323314    
Med.Statin.LLDyes         -5.915e-01  3.857e-01  -1.533 0.125200    
Med.all.antiplateletyes   -8.789e-01  6.296e-01  -1.396 0.162715    
GFR_MDRD                   7.783e-03  7.510e-03   1.036 0.300033    
BMI                       -2.873e-02  3.768e-02  -0.762 0.445850    
CAD_history                4.751e-02  2.974e-01   0.160 0.873088    
Stroke_history             1.559e+00  4.159e-01   3.748 0.000178 ***
Peripheral.interv         -3.233e-01  3.174e-01  -1.018 0.308453    
stenose50-70%             -1.358e+01  1.461e+03  -0.009 0.992580    
stenose70-90%             -1.484e+01  1.461e+03  -0.010 0.991893    
stenose90-99%             -1.492e+01  1.461e+03  -0.010 0.991852    
stenose100% (Occlusion)   -5.222e-01  1.682e+03   0.000 0.999752    
stenose70-99%             -7.740e-01  1.825e+03   0.000 0.999662    
LDL_final                  1.077e-01  1.432e-01   0.752 0.452007    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 437.63  on 621  degrees of freedom
Residual deviance: 394.25  on 602  degrees of freedom
AIC: 434.25

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.213474 
Standard error............: 0.134149 
Odds ratio (effect size)..: 0.808 
Lower 95% CI..............: 0.621 
Upper 95% CI..............: 1.051 
Z-value...................: -1.59132 
P-value...................: 0.1115376 
Hosmer and Lemeshow r^2...: 0.099128 
Cox and Snell r^2.........: 0.067368 
Nagelkerke's pseudo r^2...: 0.133352 
Sample size of AE DB......: 2388 
Sample size of model......: 622 
Missing data %............: 73.9531 

Analysis of MCP1_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Med.Statin.LLD + 
    Med.all.antiplatelet + Stroke_history, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)        Med.Statin.LLDyes  Med.all.antiplateletyes           Stroke_history  
                 3.2384                  -0.6894                  -0.9942                   1.6182  

Degrees of Freedom: 643 Total (i.e. Null);  640 Residual
Null Deviance:      447 
Residual Deviance: 416.9    AIC: 424.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + LDL_final, family = binomial(link = "logit"), data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0043   0.2305   0.3632   0.5759   1.0192  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.601e+01  1.223e+03   0.013  0.98956    
currentDF[, PROTEIN]       1.380e-01  1.256e-01   1.099  0.27189    
Age                        2.121e-02  1.665e-02   1.274  0.20279    
Gendermale                -2.311e-01  3.001e-01  -0.770  0.44137    
Hypertension.compositeyes -2.425e-01  4.705e-01  -0.515  0.60630    
DiabetesStatusDiabetes     1.947e-01  3.318e-01   0.587  0.55735    
SmokerCurrentyes           2.765e-01  2.932e-01   0.943  0.34562    
Med.Statin.LLDyes         -5.572e-01  3.798e-01  -1.467  0.14235    
Med.all.antiplateletyes   -9.251e-01  6.263e-01  -1.477  0.13967    
GFR_MDRD                   1.031e-02  7.351e-03   1.402  0.16085    
BMI                       -1.427e-02  3.488e-02  -0.409  0.68230    
CAD_history                3.312e-02  2.894e-01   0.114  0.90887    
Stroke_history             1.537e+00  4.161e-01   3.695  0.00022 ***
Peripheral.interv         -3.618e-01  3.123e-01  -1.158  0.24670    
stenose50-70%             -1.342e+01  1.223e+03  -0.011  0.99124    
stenose70-90%             -1.472e+01  1.223e+03  -0.012  0.99039    
stenose90-99%             -1.479e+01  1.223e+03  -0.012  0.99035    
stenose100% (Occlusion)   -3.050e-01  1.487e+03   0.000  0.99984    
stenose70-99%             -7.617e-01  1.676e+03   0.000  0.99964    
LDL_final                  1.144e-01  1.395e-01   0.821  0.41190    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 446.98  on 643  degrees of freedom
Residual deviance: 403.54  on 624  degrees of freedom
AIC: 443.54

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.138042 
Standard error............: 0.12564 
Odds ratio (effect size)..: 1.148 
Lower 95% CI..............: 0.897 
Upper 95% CI..............: 1.469 
Z-value...................: 1.098713 
P-value...................: 0.2718933 
Hosmer and Lemeshow r^2...: 0.097176 
Cox and Snell r^2.........: 0.065223 
Nagelkerke's pseudo r^2...: 0.130325 
Sample size of AE DB......: 2388 
Sample size of model......: 644 
Missing data %............: 73.03183 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 4

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, stenosis., and hsCRP.

Natural log-transformed data

First we use the natural-log transformed data.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of IL6_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    DiabetesStatus + Med.Statin.LLD + Stroke_history + Peripheral.interv, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes     DiabetesStatusDiabetes          Med.Statin.LLDyes             Stroke_history  
                   2.5245                    -0.7846                     0.7607                    -0.9893                     1.2704  
        Peripheral.interv  
                  -0.8749  

Degrees of Freedom: 227 Total (i.e. Null);  222 Residual
Null Deviance:      237.3 
Residual Deviance: 212.7    AIC: 224.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2733   0.1875   0.4731   0.7314   1.3597  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                2.050e+01  2.400e+03   0.009  0.99318   
currentDF[, PROTEIN]      -1.933e-01  1.787e-01  -1.081  0.27953   
Age                       -2.313e-02  2.555e-02  -0.906  0.36513   
Gendermale                 3.848e-01  4.022e-01   0.957  0.33877   
Hypertension.compositeyes -1.147e+00  6.318e-01  -1.816  0.06937 . 
DiabetesStatusDiabetes     8.272e-01  5.544e-01   1.492  0.13573   
SmokerCurrentyes          -1.491e-01  3.911e-01  -0.381  0.70302   
Med.Statin.LLDyes         -1.042e+00  4.415e-01  -2.360  0.01826 * 
Med.all.antiplateletyes   -6.377e-02  7.479e-01  -0.085  0.93206   
GFR_MDRD                   7.121e-03  1.166e-02   0.611  0.54139   
BMI                       -2.683e-03  4.738e-02  -0.057  0.95484   
CAD_history                1.288e-01  3.988e-01   0.323  0.74675   
Stroke_history             1.360e+00  4.785e-01   2.842  0.00448 **
Peripheral.interv         -8.959e-01  3.926e-01  -2.282  0.02248 * 
stenose50-70%             -1.757e+01  2.400e+03  -0.007  0.99416   
stenose70-90%             -1.633e+01  2.400e+03  -0.007  0.99457   
stenose90-99%             -1.576e+01  2.400e+03  -0.007  0.99476   
stenose100% (Occlusion)   -1.483e+00  2.703e+03  -0.001  0.99956   
hsCRP_plasma               3.120e-03  5.019e-03   0.622  0.53423   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 237.30  on 227  degrees of freedom
Residual deviance: 204.33  on 209  degrees of freedom
AIC: 242.33

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.193256 
Standard error............: 0.178712 
Odds ratio (effect size)..: 0.824 
Lower 95% CI..............: 0.581 
Upper 95% CI..............: 1.17 
Z-value...................: -1.081383 
P-value...................: 0.2795267 
Hosmer and Lemeshow r^2...: 0.138941 
Cox and Snell r^2.........: 0.134639 
Nagelkerke's pseudo r^2...: 0.208154 
Sample size of AE DB......: 2388 
Sample size of model......: 228 
Missing data %............: 90.45226 

Analysis of MCP1_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    DiabetesStatus + Med.Statin.LLD + Stroke_history + Peripheral.interv, 
    family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes     DiabetesStatusDiabetes          Med.Statin.LLDyes             Stroke_history  
                   2.7455                    -0.9488                     0.7094                    -0.9165                     1.3830  
        Peripheral.interv  
                  -0.7997  

Degrees of Freedom: 278 Total (i.e. Null);  273 Residual
Null Deviance:      271.3 
Residual Deviance: 242.8    AIC: 254.8

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5328   0.2397   0.4550   0.6805   1.4332  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.853e+01  2.400e+03   0.008  0.99384   
currentDF[, PROTEIN]       1.639e-01  2.012e-01   0.815  0.41522   
Age                       -2.290e-02  2.336e-02  -0.980  0.32696   
Gendermale                 1.420e-01  3.780e-01   0.376  0.70722   
Hypertension.compositeyes -1.187e+00  6.013e-01  -1.975  0.04828 * 
DiabetesStatusDiabetes     7.247e-01  4.877e-01   1.486  0.13728   
SmokerCurrentyes          -1.354e-01  3.633e-01  -0.373  0.70942   
Med.Statin.LLDyes         -9.441e-01  4.386e-01  -2.152  0.03136 * 
Med.all.antiplateletyes   -2.041e-01  7.140e-01  -0.286  0.77496   
GFR_MDRD                  -2.775e-03  1.030e-02  -0.269  0.78761   
BMI                        1.860e-02  4.290e-02   0.434  0.66456   
CAD_history                1.981e-01  3.695e-01   0.536  0.59196   
Stroke_history             1.529e+00  4.694e-01   3.258  0.00112 **
Peripheral.interv         -8.343e-01  3.707e-01  -2.251  0.02439 * 
stenose50-70%             -1.531e+01  2.400e+03  -0.006  0.99491   
stenose70-90%             -1.543e+01  2.400e+03  -0.006  0.99487   
stenose90-99%             -1.481e+01  2.400e+03  -0.006  0.99508   
stenose100% (Occlusion)   -3.712e-01  2.690e+03   0.000  0.99989   
hsCRP_plasma               1.275e-03  3.465e-03   0.368  0.71299   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 271.28  on 278  degrees of freedom
Residual deviance: 236.26  on 260  degrees of freedom
AIC: 274.26

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.163894 
Standard error............: 0.201161 
Odds ratio (effect size)..: 1.178 
Lower 95% CI..............: 0.794 
Upper 95% CI..............: 1.747 
Z-value...................: 0.814742 
P-value...................: 0.4152199 
Hosmer and Lemeshow r^2...: 0.129113 
Cox and Snell r^2.........: 0.117981 
Nagelkerke's pseudo r^2...: 0.18974 
Sample size of AE DB......: 2388 
Sample size of model......: 279 
Missing data %............: 88.31658 

Analysis of IL6_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + CAD_history + Stroke_history + 
    Peripheral.interv, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)         SmokerCurrentyes        Med.Statin.LLDyes  Med.all.antiplateletyes              CAD_history  
                 3.5223                   0.4197                  -0.7448                  -1.3388                   0.4457  
         Stroke_history        Peripheral.interv  
                 1.0675                  -0.6632  

Degrees of Freedom: 618 Total (i.e. Null);  612 Residual
Null Deviance:      449.1 
Residual Deviance: 418  AIC: 432

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0434   0.2574   0.4128   0.5582   1.0607  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.681e+01  1.296e+03   0.013  0.98965   
currentDF[, PROTEIN]       4.923e-02  9.140e-02   0.539  0.59013   
Age                        2.097e-02  1.651e-02   1.270  0.20406   
Gendermale                -1.581e-01  2.913e-01  -0.543  0.58724   
Hypertension.compositeyes -2.427e-01  4.429e-01  -0.548  0.58375   
DiabetesStatusDiabetes     1.106e-01  3.301e-01   0.335  0.73760   
SmokerCurrentyes           4.600e-01  3.004e-01   1.532  0.12562   
Med.Statin.LLDyes         -6.978e-01  3.703e-01  -1.885  0.05948 . 
Med.all.antiplateletyes   -1.297e+00  7.501e-01  -1.729  0.08374 . 
GFR_MDRD                   7.343e-03  7.465e-03   0.984  0.32531   
BMI                       -1.178e-02  3.354e-02  -0.351  0.72539   
CAD_history                4.664e-01  3.078e-01   1.516  0.12962   
Stroke_history             9.369e-01  3.522e-01   2.660  0.00781 **
Peripheral.interv         -6.347e-01  2.947e-01  -2.153  0.03129 * 
stenose50-70%             -1.382e+01  1.296e+03  -0.011  0.99149   
stenose70-90%             -1.461e+01  1.296e+03  -0.011  0.99100   
stenose90-99%             -1.459e+01  1.296e+03  -0.011  0.99101   
stenose100% (Occlusion)   -8.537e-01  1.729e+03   0.000  0.99961   
stenose70-99%             -2.552e-01  1.603e+03   0.000  0.99987   
hsCRP_plasma               2.006e-03  4.527e-03   0.443  0.65764   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 449.13  on 618  degrees of freedom
Residual deviance: 410.28  on 599  degrees of freedom
AIC: 450.28

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.049231 
Standard error............: 0.091399 
Odds ratio (effect size)..: 1.05 
Lower 95% CI..............: 0.878 
Upper 95% CI..............: 1.257 
Z-value...................: 0.538646 
P-value...................: 0.5901313 
Hosmer and Lemeshow r^2...: 0.086503 
Cox and Snell r^2.........: 0.060835 
Nagelkerke's pseudo r^2...: 0.117908 
Sample size of AE DB......: 2388 
Sample size of model......: 619 
Missing data %............: 74.07873 

Analysis of IL6R_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + Stroke_history + 
    Peripheral.interv, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)                      Age         SmokerCurrentyes        Med.Statin.LLDyes  Med.all.antiplateletyes  
                1.76707                  0.02495                  0.57986                 -0.67730                 -1.31015  
         Stroke_history        Peripheral.interv  
                0.94026                 -0.52936  

Degrees of Freedom: 623 Total (i.e. Null);  617 Residual
Null Deviance:      466.3 
Residual Deviance: 433.7    AIC: 447.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0583   0.2511   0.4164   0.5773   1.1120  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.586e+01  1.568e+03   0.010  0.99193   
currentDF[, PROTEIN]      -1.244e-01  1.119e-01  -1.112  0.26595   
Age                        2.752e-02  1.627e-02   1.691  0.09078 . 
Gendermale                -1.480e-01  2.847e-01  -0.520  0.60320   
Hypertension.compositeyes -1.468e-01  4.214e-01  -0.348  0.72755   
DiabetesStatusDiabetes     1.144e-01  3.217e-01   0.356  0.72215   
SmokerCurrentyes           5.730e-01  2.950e-01   1.942  0.05213 . 
Med.Statin.LLDyes         -7.764e-01  3.715e-01  -2.090  0.03662 * 
Med.all.antiplateletyes   -1.315e+00  7.495e-01  -1.755  0.07927 . 
GFR_MDRD                   8.654e-03  7.329e-03   1.181  0.23774   
BMI                       -3.648e-02  3.444e-02  -1.059  0.28940   
CAD_history                3.017e-01  2.950e-01   1.023  0.30641   
Stroke_history             9.386e-01  3.373e-01   2.783  0.00539 **
Peripheral.interv         -5.299e-01  2.939e-01  -1.803  0.07135 . 
stenose50-70%             -1.320e+01  1.568e+03  -0.008  0.99328   
stenose70-90%             -1.409e+01  1.568e+03  -0.009  0.99283   
stenose90-99%             -1.395e+01  1.568e+03  -0.009  0.99290   
stenose100% (Occlusion)   -3.580e-01  1.942e+03   0.000  0.99985   
stenose50-99%             -7.396e-02  2.294e+03   0.000  0.99997   
stenose70-99%              2.370e-02  1.835e+03   0.000  0.99999   
hsCRP_plasma               1.943e-03  4.408e-03   0.441  0.65942   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 466.30  on 623  degrees of freedom
Residual deviance: 423.43  on 603  degrees of freedom
AIC: 465.43

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.12443 
Standard error............: 0.111854 
Odds ratio (effect size)..: 0.883 
Lower 95% CI..............: 0.709 
Upper 95% CI..............: 1.099 
Z-value...................: -1.112435 
P-value...................: 0.2659513 
Hosmer and Lemeshow r^2...: 0.091937 
Cox and Snell r^2.........: 0.066396 
Nagelkerke's pseudo r^2...: 0.126144 
Sample size of AE DB......: 2388 
Sample size of model......: 624 
Missing data %............: 73.86935 

Analysis of MCP1_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + Stroke_history + 
    Peripheral.interv, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)                      Age         SmokerCurrentyes        Med.Statin.LLDyes  Med.all.antiplateletyes  
                1.84941                  0.02409                  0.58172                 -0.67198                 -1.32001  
         Stroke_history        Peripheral.interv  
                0.93466                 -0.49342  

Degrees of Freedom: 642 Total (i.e. Null);  636 Residual
Null Deviance:      475.2 
Residual Deviance: 442.7    AIC: 456.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.1163   0.2488   0.4146   0.5787   1.1315  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.621e+01  1.277e+03   0.013   0.9899  
currentDF[, PROTEIN]       1.112e-01  9.288e-02   1.197   0.2313  
Age                        2.881e-02  1.614e-02   1.784   0.0744 .
Gendermale                -2.003e-01  2.832e-01  -0.707   0.4794  
Hypertension.compositeyes -1.115e-01  4.203e-01  -0.265   0.7908  
DiabetesStatusDiabetes     1.904e-01  3.206e-01   0.594   0.5527  
SmokerCurrentyes           5.819e-01  2.924e-01   1.990   0.0466 *
Med.Statin.LLDyes         -7.131e-01  3.688e-01  -1.934   0.0531 .
Med.all.antiplateletyes   -1.328e+00  7.486e-01  -1.774   0.0761 .
GFR_MDRD                   9.876e-03  7.390e-03   1.336   0.1814  
BMI                       -1.873e-02  3.229e-02  -0.580   0.5619  
CAD_history                3.350e-01  2.910e-01   1.151   0.2496  
Stroke_history             8.691e-01  3.381e-01   2.571   0.0102 *
Peripheral.interv         -5.305e-01  2.903e-01  -1.827   0.0677 .
stenose50-70%             -1.385e+01  1.277e+03  -0.011   0.9914  
stenose70-90%             -1.472e+01  1.277e+03  -0.012   0.9908  
stenose90-99%             -1.461e+01  1.277e+03  -0.011   0.9909  
stenose100% (Occlusion)   -7.127e-01  1.716e+03   0.000   0.9997  
stenose50-99%             -7.489e-01  2.116e+03   0.000   0.9997  
stenose70-99%             -5.020e-01  1.594e+03   0.000   0.9997  
hsCRP_plasma               2.222e-03  4.883e-03   0.455   0.6491  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 475.20  on 642  degrees of freedom
Residual deviance: 432.23  on 622  degrees of freedom
AIC: 474.23

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.111174 
Standard error............: 0.092877 
Odds ratio (effect size)..: 1.118 
Lower 95% CI..............: 0.932 
Upper 95% CI..............: 1.341 
Z-value...................: 1.196996 
P-value...................: 0.2313082 
Hosmer and Lemeshow r^2...: 0.09043 
Cox and Snell r^2.........: 0.064647 
Nagelkerke's pseudo r^2...: 0.123744 
Sample size of AE DB......: 2388 
Sample size of model......: 643 
Missing data %............: 73.0737 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of IL6_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Hypertension.composite + Med.Statin.LLD + Stroke_history + 
    Peripheral.interv, family = binomial(link = "logit"), data = currentDF)

Coefficients:
              (Intercept)       currentDF[, PROTEIN]  Hypertension.compositeyes          Med.Statin.LLDyes             Stroke_history  
                   2.7478                    -0.2817                    -0.9979                    -0.8465                     1.3302  
        Peripheral.interv  
                  -0.7109  

Degrees of Freedom: 268 Total (i.e. Null);  263 Residual
Null Deviance:      275.2 
Residual Deviance: 247.3    AIC: 259.3

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3945   0.1672   0.4741   0.7406   1.2867  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.901e+01  2.400e+03   0.008  0.99368   
currentDF[, PROTEIN]      -3.246e-01  1.770e-01  -1.834  0.06665 . 
Age                       -1.434e-02  2.272e-02  -0.631  0.52803   
Gendermale                 1.039e-01  3.677e-01   0.283  0.77755   
Hypertension.compositeyes -1.357e+00  6.078e-01  -2.233  0.02555 * 
DiabetesStatusDiabetes     3.891e-01  4.643e-01   0.838  0.40205   
SmokerCurrentyes           1.193e-01  3.606e-01   0.331  0.74072   
Med.Statin.LLDyes         -9.307e-01  4.151e-01  -2.242  0.02494 * 
Med.all.antiplateletyes   -2.950e-01  7.226e-01  -0.408  0.68311   
GFR_MDRD                   7.384e-03  1.048e-02   0.704  0.48124   
BMI                        2.260e-02  4.391e-02   0.515  0.60684   
CAD_history                2.683e-01  3.647e-01   0.736  0.46200   
Stroke_history             1.403e+00  4.646e-01   3.020  0.00253 **
Peripheral.interv         -7.912e-01  3.610e-01  -2.192  0.02841 * 
stenose50-70%             -1.700e+01  2.400e+03  -0.007  0.99435   
stenose70-90%             -1.646e+01  2.400e+03  -0.007  0.99453   
stenose90-99%             -1.579e+01  2.400e+03  -0.007  0.99475   
stenose100% (Occlusion)   -1.474e+00  2.645e+03  -0.001  0.99956   
hsCRP_plasma               2.218e-03  4.303e-03   0.515  0.60621   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 275.20  on 268  degrees of freedom
Residual deviance: 238.94  on 250  degrees of freedom
AIC: 276.94

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.324614 
Standard error............: 0.176996 
Odds ratio (effect size)..: 0.723 
Lower 95% CI..............: 0.511 
Upper 95% CI..............: 1.023 
Z-value...................: -1.834017 
P-value...................: 0.06665144 
Hosmer and Lemeshow r^2...: 0.131783 
Cox and Snell r^2.........: 0.12613 
Nagelkerke's pseudo r^2...: 0.196921 
Sample size of AE DB......: 2388 
Sample size of model......: 269 
Missing data %............: 88.73534 

Analysis of MCP1_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    Med.Statin.LLD + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes          Med.Statin.LLDyes             Stroke_history          Peripheral.interv  
                   2.7459                    -0.8823                    -0.8903                     1.3551                    -0.7452  

Degrees of Freedom: 282 Total (i.e. Null);  278 Residual
Null Deviance:      278.7 
Residual Deviance: 251.5    AIC: 261.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.5354   0.2446   0.4647   0.7125   1.3983  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.852e+01  2.400e+03   0.008  0.99384   
currentDF[, PROTEIN]       2.083e-01  1.698e-01   1.227  0.21985   
Age                       -1.254e-02  2.242e-02  -0.559  0.57605   
Gendermale                 8.477e-02  3.694e-01   0.229  0.81849   
Hypertension.compositeyes -1.129e+00  5.986e-01  -1.886  0.05935 . 
DiabetesStatusDiabetes     4.580e-01  4.514e-01   1.015  0.31029   
SmokerCurrentyes          -6.676e-03  3.575e-01  -0.019  0.98510   
Med.Statin.LLDyes         -9.166e-01  4.335e-01  -2.115  0.03447 * 
Med.all.antiplateletyes   -5.242e-01  7.147e-01  -0.733  0.46329   
GFR_MDRD                  -1.484e-03  1.014e-02  -0.146  0.88363   
BMI                        2.330e-02  4.168e-02   0.559  0.57614   
CAD_history                2.614e-01  3.661e-01   0.714  0.47514   
Stroke_history             1.473e+00  4.626e-01   3.185  0.00145 **
Peripheral.interv         -7.791e-01  3.664e-01  -2.126  0.03349 * 
stenose50-70%             -1.515e+01  2.400e+03  -0.006  0.99496   
stenose70-90%             -1.530e+01  2.400e+03  -0.006  0.99491   
stenose90-99%             -1.479e+01  2.400e+03  -0.006  0.99508   
stenose100% (Occlusion)   -3.797e-01  2.689e+03   0.000  0.99989   
hsCRP_plasma               1.105e-03  3.283e-03   0.337  0.73637   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 278.73  on 282  degrees of freedom
Residual deviance: 243.89  on 264  degrees of freedom
AIC: 281.89

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.208288 
Standard error............: 0.169766 
Odds ratio (effect size)..: 1.232 
Lower 95% CI..............: 0.883 
Upper 95% CI..............: 1.718 
Z-value...................: 1.226914 
P-value...................: 0.2198548 
Hosmer and Lemeshow r^2...: 0.124996 
Cox and Snell r^2.........: 0.115836 
Nagelkerke's pseudo r^2...: 0.184884 
Sample size of AE DB......: 2388 
Sample size of model......: 283 
Missing data %............: 88.14908 

Analysis of IL6_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + CAD_history + Stroke_history + 
    Peripheral.interv, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)         SmokerCurrentyes        Med.Statin.LLDyes  Med.all.antiplateletyes              CAD_history  
                 3.5223                   0.4197                  -0.7448                  -1.3388                   0.4457  
         Stroke_history        Peripheral.interv  
                 1.0675                  -0.6632  

Degrees of Freedom: 618 Total (i.e. Null);  612 Residual
Null Deviance:      449.1 
Residual Deviance: 418  AIC: 432

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0372   0.2573   0.4134   0.5576   1.0672  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.665e+01  1.294e+03   0.013   0.9897   
currentDF[, PROTEIN]       8.355e-02  1.349e-01   0.619   0.5358   
Age                        2.094e-02  1.651e-02   1.268   0.2047   
Gendermale                -1.585e-01  2.913e-01  -0.544   0.5864   
Hypertension.compositeyes -2.428e-01  4.429e-01  -0.548   0.5836   
DiabetesStatusDiabetes     1.095e-01  3.301e-01   0.332   0.7400   
SmokerCurrentyes           4.602e-01  3.004e-01   1.532   0.1255   
Med.Statin.LLDyes         -6.981e-01  3.703e-01  -1.885   0.0594 . 
Med.all.antiplateletyes   -1.301e+00  7.504e-01  -1.734   0.0828 . 
GFR_MDRD                   7.369e-03  7.470e-03   0.987   0.3238   
BMI                       -1.151e-02  3.352e-02  -0.343   0.7313   
CAD_history                4.680e-01  3.075e-01   1.522   0.1280   
Stroke_history             9.339e-01  3.521e-01   2.652   0.0080 **
Peripheral.interv         -6.329e-01  2.948e-01  -2.147   0.0318 * 
stenose50-70%             -1.383e+01  1.294e+03  -0.011   0.9915   
stenose70-90%             -1.462e+01  1.294e+03  -0.011   0.9910   
stenose90-99%             -1.460e+01  1.294e+03  -0.011   0.9910   
stenose100% (Occlusion)   -8.536e-01  1.728e+03   0.000   0.9996   
stenose70-99%             -2.603e-01  1.601e+03   0.000   0.9999   
hsCRP_plasma               2.019e-03  4.558e-03   0.443   0.6577   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 449.13  on 618  degrees of freedom
Residual deviance: 410.18  on 599  degrees of freedom
AIC: 450.18

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.08355 
Standard error............: 0.134925 
Odds ratio (effect size)..: 1.087 
Lower 95% CI..............: 0.835 
Upper 95% CI..............: 1.416 
Z-value...................: 0.619236 
P-value...................: 0.5357609 
Hosmer and Lemeshow r^2...: 0.086712 
Cox and Snell r^2.........: 0.060977 
Nagelkerke's pseudo r^2...: 0.118184 
Sample size of AE DB......: 2388 
Sample size of model......: 619 
Missing data %............: 74.07873 

Analysis of IL6R_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + Stroke_history + 
    Peripheral.interv, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)                      Age         SmokerCurrentyes        Med.Statin.LLDyes  Med.all.antiplateletyes  
                1.76707                  0.02495                  0.57986                 -0.67730                 -1.31015  
         Stroke_history        Peripheral.interv  
                0.94026                 -0.52936  

Degrees of Freedom: 623 Total (i.e. Null);  617 Residual
Null Deviance:      466.3 
Residual Deviance: 433.7    AIC: 447.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.0600   0.2532   0.4185   0.5752   1.1086  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                1.617e+01  1.557e+03   0.010   0.9917   
currentDF[, PROTEIN]      -1.120e-01  1.242e-01  -0.902   0.3670   
Age                        2.750e-02  1.628e-02   1.689   0.0912 . 
Gendermale                -1.439e-01  2.846e-01  -0.506   0.6132   
Hypertension.compositeyes -1.462e-01  4.213e-01  -0.347   0.7285   
DiabetesStatusDiabetes     1.245e-01  3.212e-01   0.388   0.6983   
SmokerCurrentyes           5.751e-01  2.950e-01   1.950   0.0512 . 
Med.Statin.LLDyes         -7.720e-01  3.717e-01  -2.077   0.0378 * 
Med.all.antiplateletyes   -1.318e+00  7.493e-01  -1.758   0.0787 . 
GFR_MDRD                   8.610e-03  7.337e-03   1.174   0.2406   
BMI                       -3.572e-02  3.447e-02  -1.036   0.3000   
CAD_history                3.093e-01  2.947e-01   1.049   0.2940   
Stroke_history             9.321e-01  3.372e-01   2.764   0.0057 **
Peripheral.interv         -5.352e-01  2.940e-01  -1.820   0.0687 . 
stenose50-70%             -1.328e+01  1.557e+03  -0.009   0.9932   
stenose70-90%             -1.417e+01  1.557e+03  -0.009   0.9927   
stenose90-99%             -1.404e+01  1.557e+03  -0.009   0.9928   
stenose100% (Occlusion)   -4.252e-01  1.933e+03   0.000   0.9998   
stenose50-99%             -1.688e-01  2.287e+03   0.000   0.9999   
stenose70-99%              1.041e-04  1.825e+03   0.000   1.0000   
hsCRP_plasma               1.943e-03  4.401e-03   0.442   0.6588   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 466.30  on 623  degrees of freedom
Residual deviance: 423.92  on 603  degrees of freedom
AIC: 465.92

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.112022 
Standard error............: 0.124189 
Odds ratio (effect size)..: 0.894 
Lower 95% CI..............: 0.701 
Upper 95% CI..............: 1.14 
Z-value...................: -0.902032 
P-value...................: 0.36704 
Hosmer and Lemeshow r^2...: 0.090899 
Cox and Snell r^2.........: 0.065671 
Nagelkerke's pseudo r^2...: 0.124768 
Sample size of AE DB......: 2388 
Sample size of model......: 624 
Missing data %............: 73.86935 

Analysis of MCP1_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerCurrent + 
    Med.Statin.LLD + Med.all.antiplatelet + Stroke_history + 
    Peripheral.interv, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)                      Age         SmokerCurrentyes        Med.Statin.LLDyes  Med.all.antiplateletyes  
                1.84941                  0.02409                  0.58172                 -0.67198                 -1.32001  
         Stroke_history        Peripheral.interv  
                0.93466                 -0.49342  

Degrees of Freedom: 642 Total (i.e. Null);  636 Residual
Null Deviance:      475.2 
Residual Deviance: 442.7    AIC: 456.7

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-3.1203   0.2480   0.4177   0.5780   1.1392  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)  
(Intercept)                1.605e+01  1.276e+03   0.013   0.9900  
currentDF[, PROTEIN]       1.655e-01  1.228e-01   1.347   0.1780  
Age                        2.913e-02  1.617e-02   1.801   0.0716 .
Gendermale                -2.021e-01  2.832e-01  -0.714   0.4755  
Hypertension.compositeyes -1.069e-01  4.204e-01  -0.254   0.7992  
DiabetesStatusDiabetes     1.868e-01  3.205e-01   0.583   0.5600  
SmokerCurrentyes           5.855e-01  2.925e-01   2.002   0.0453 *
Med.Statin.LLDyes         -7.137e-01  3.689e-01  -1.935   0.0530 .
Med.all.antiplateletyes   -1.331e+00  7.487e-01  -1.777   0.0755 .
GFR_MDRD                   9.981e-03  7.402e-03   1.348   0.1775  
BMI                       -1.865e-02  3.227e-02  -0.578   0.5634  
CAD_history                3.335e-01  2.908e-01   1.147   0.2514  
Stroke_history             8.659e-01  3.381e-01   2.561   0.0104 *
Peripheral.interv         -5.307e-01  2.904e-01  -1.828   0.0676 .
stenose50-70%             -1.385e+01  1.276e+03  -0.011   0.9913  
stenose70-90%             -1.473e+01  1.276e+03  -0.012   0.9908  
stenose90-99%             -1.461e+01  1.276e+03  -0.011   0.9909  
stenose100% (Occlusion)   -7.054e-01  1.716e+03   0.000   0.9997  
stenose50-99%             -7.583e-01  2.116e+03   0.000   0.9997  
stenose70-99%             -5.285e-01  1.593e+03   0.000   0.9997  
hsCRP_plasma               2.253e-03  4.931e-03   0.457   0.6478  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 475.20  on 642  degrees of freedom
Residual deviance: 431.82  on 622  degrees of freedom
AIC: 473.82

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.165464 
Standard error............: 0.12284 
Odds ratio (effect size)..: 1.18 
Lower 95% CI..............: 0.927 
Upper 95% CI..............: 1.501 
Z-value...................: 1.346985 
P-value...................: 0.177985 
Hosmer and Lemeshow r^2...: 0.091292 
Cox and Snell r^2.........: 0.065243 
Nagelkerke's pseudo r^2...: 0.124883 
Sample size of AE DB......: 2388 
Sample size of model......: 643 
Missing data %............: 73.0737 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Model 5

In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, CAD history, stroke history, peripheral interventions, stenosis., and IL6 in plaques.

Natural log-transformed data

First we use the natural-log transformed data.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_LN, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of IL6_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    Med.Statin.LLD + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes          Med.Statin.LLDyes             Stroke_history          Peripheral.interv  
                   3.1233                    -1.1555                    -0.5724                     1.4408                    -0.9856  

Degrees of Freedom: 318 Total (i.e. Null);  314 Residual
Null Deviance:      273.6 
Residual Deviance: 245.6    AIC: 255.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.6954   0.2310   0.4158   0.6161   1.3095  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)   
(Intercept)                2.024e+01  1.635e+03   0.012  0.99012   
currentDF[, PROTEIN]       8.990e-02  1.712e-01   0.525  0.59955   
Age                       -1.184e-02  2.234e-02  -0.530  0.59604   
Gendermale                 3.202e-01  3.733e-01   0.858  0.39092   
Hypertension.compositeyes -1.258e+00  7.819e-01  -1.609  0.10758   
DiabetesStatusDiabetes     1.895e-01  4.358e-01   0.435  0.66367   
SmokerCurrentyes          -6.166e-01  3.694e-01  -1.669  0.09504 . 
Med.Statin.LLDyes         -5.971e-01  4.308e-01  -1.386  0.16580   
Med.all.antiplateletyes   -7.127e-01  6.897e-01  -1.033  0.30149   
GFR_MDRD                   3.517e-03  1.020e-02   0.345  0.73016   
BMI                       -2.071e-02  4.695e-02  -0.441  0.65911   
CAD_history               -1.294e-01  3.774e-01  -0.343  0.73165   
Stroke_history             1.525e+00  4.737e-01   3.219  0.00128 **
Peripheral.interv         -9.504e-01  3.695e-01  -2.572  0.01010 * 
stenose50-70%             -1.466e+01  1.635e+03  -0.009  0.99284   
stenose70-90%             -1.547e+01  1.635e+03  -0.009  0.99245   
stenose90-99%             -1.514e+01  1.635e+03  -0.009  0.99261   
stenose100% (Occlusion)   -1.725e+00  2.046e+03  -0.001  0.99933   
IL6_pg_ug_2015_LN          1.081e-01  1.190e-01   0.908  0.36387   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 273.65  on 318  degrees of freedom
Residual deviance: 236.90  on 300  degrees of freedom
AIC: 274.9

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.089896 
Standard error............: 0.171214 
Odds ratio (effect size)..: 1.094 
Lower 95% CI..............: 0.782 
Upper 95% CI..............: 1.53 
Z-value...................: 0.525049 
P-value...................: 0.5995494 
Hosmer and Lemeshow r^2...: 0.134276 
Cox and Snell r^2.........: 0.108799 
Nagelkerke's pseudo r^2...: 0.188914 
Sample size of AE DB......: 2388 
Sample size of model......: 319 
Missing data %............: 86.64154 

Analysis of MCP1_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes             Stroke_history          Peripheral.interv  
                    3.046                     -1.489                      1.618                     -1.024  

Degrees of Freedom: 390 Total (i.e. Null);  387 Residual
Null Deviance:      317.6 
Residual Deviance: 282.5    AIC: 290.5

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.7663   0.2017   0.3715   0.6050   1.4386  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.896e+01  1.643e+03   0.012 0.990793    
currentDF[, PROTEIN]       1.215e-01  1.958e-01   0.621 0.534683    
Age                        2.906e-03  2.022e-02   0.144 0.885728    
Gendermale                -6.048e-03  3.525e-01  -0.017 0.986312    
Hypertension.compositeyes -1.665e+00  7.758e-01  -2.146 0.031910 *  
DiabetesStatusDiabetes     1.615e-01  3.926e-01   0.411 0.680858    
SmokerCurrentyes          -4.023e-01  3.387e-01  -1.188 0.234883    
Med.Statin.LLDyes         -4.024e-01  4.046e-01  -0.995 0.319878    
Med.all.antiplateletyes   -8.924e-01  6.929e-01  -1.288 0.197789    
GFR_MDRD                   9.595e-04  9.306e-03   0.103 0.917883    
BMI                        4.773e-05  4.263e-02   0.001 0.999107    
CAD_history               -1.512e-01  3.476e-01  -0.435 0.663556    
Stroke_history             1.687e+00  4.656e-01   3.623 0.000291 ***
Peripheral.interv         -1.048e+00  3.573e-01  -2.935 0.003338 ** 
stenose50-70%             -1.416e+01  1.643e+03  -0.009 0.993126    
stenose70-90%             -1.554e+01  1.643e+03  -0.009 0.992453    
stenose90-99%             -1.488e+01  1.643e+03  -0.009 0.992774    
stenose100% (Occlusion)   -1.543e+00  1.975e+03  -0.001 0.999376    
IL6_pg_ug_2015_LN          4.199e-02  1.094e-01   0.384 0.701204    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 317.62  on 390  degrees of freedom
Residual deviance: 271.14  on 372  degrees of freedom
AIC: 309.14

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.121548 
Standard error............: 0.195769 
Odds ratio (effect size)..: 1.129 
Lower 95% CI..............: 0.769 
Upper 95% CI..............: 1.657 
Z-value...................: 0.620873 
P-value...................: 0.534683 
Hosmer and Lemeshow r^2...: 0.146359 
Cox and Snell r^2.........: 0.112097 
Nagelkerke's pseudo r^2...: 0.201548 
Sample size of AE DB......: 2388 
Sample size of model......: 391 
Missing data %............: 83.62647 

Analysis of IL6_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    Med.all.antiplatelet + GFR_MDRD + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)                      Age               Gendermale  Med.all.antiplateletyes                 GFR_MDRD  
              15.387606                 0.027762                -0.453161                -0.916880                 0.008104  
         Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  
               1.166727                -0.673292               -13.748714               -14.870639               -14.701591  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
              -0.331218               -16.941956                -0.167408  

Degrees of Freedom: 1008 Total (i.e. Null);  996 Residual
Null Deviance:      707.6 
Residual Deviance: 652.1    AIC: 678.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8226   0.2652   0.4017   0.5590   0.9792  

Coefficients: (1 not defined because of singularities)
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.565e+01  1.027e+03   0.015 0.987838    
currentDF[, PROTEIN]       4.009e-02  7.151e-02   0.561 0.575025    
Age                        3.157e-02  1.296e-02   2.437 0.014805 *  
Gendermale                -4.000e-01  2.443e-01  -1.637 0.101631    
Hypertension.compositeyes -2.988e-01  3.648e-01  -0.819 0.412745    
DiabetesStatusDiabetes    -8.362e-03  2.509e-01  -0.033 0.973407    
SmokerCurrentyes           1.551e-01  2.332e-01   0.665 0.506105    
Med.Statin.LLDyes         -1.772e-01  2.732e-01  -0.649 0.516525    
Med.all.antiplateletyes   -9.046e-01  4.485e-01  -2.017 0.043704 *  
GFR_MDRD                   7.273e-03  5.677e-03   1.281 0.200167    
BMI                        9.375e-04  2.893e-02   0.032 0.974147    
CAD_history               -1.784e-01  2.263e-01  -0.788 0.430588    
Stroke_history             1.108e+00  2.862e-01   3.872 0.000108 ***
Peripheral.interv         -6.278e-01  2.398e-01  -2.618 0.008851 ** 
stenose50-70%             -1.368e+01  1.027e+03  -0.013 0.989373    
stenose70-90%             -1.486e+01  1.027e+03  -0.014 0.988456    
stenose90-99%             -1.469e+01  1.027e+03  -0.014 0.988585    
stenose100% (Occlusion)   -4.360e-01  1.306e+03   0.000 0.999734    
stenose50-99%             -1.691e+01  1.027e+03  -0.016 0.986861    
stenose70-99%             -1.378e-01  1.233e+03   0.000 0.999911    
IL6_pg_ug_2015_LN                 NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 707.63  on 1008  degrees of freedom
Residual deviance: 648.67  on  989  degrees of freedom
AIC: 688.67

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.040092 
Standard error............: 0.071508 
Odds ratio (effect size)..: 1.041 
Lower 95% CI..............: 0.905 
Upper 95% CI..............: 1.198 
Z-value...................: 0.560667 
P-value...................: 0.5750247 
Hosmer and Lemeshow r^2...: 0.083324 
Cox and Snell r^2.........: 0.056762 
Nagelkerke's pseudo r^2...: 0.112607 
Sample size of AE DB......: 2388 
Sample size of model......: 1009 
Missing data %............: 57.74707 

Analysis of IL6R_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Med.all.antiplatelet + Stroke_history + Peripheral.interv + 
    IL6_pg_ug_2015_LN, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age               Gendermale  Med.all.antiplateletyes  
                1.22809                 -0.29703                  0.02273                 -0.38267                 -0.74254  
         Stroke_history        Peripheral.interv        IL6_pg_ug_2015_LN  
                1.09881                 -0.56737                  0.13789  

Degrees of Freedom: 975 Total (i.e. Null);  968 Residual
Null Deviance:      687.4 
Residual Deviance: 639.4    AIC: 655.4

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.7313   0.2616   0.3936   0.5448   1.0748  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.547e+01  1.142e+03   0.014 0.989188    
currentDF[, PROTEIN]      -2.799e-01  1.087e-01  -2.575 0.010020 *  
Age                        3.013e-02  1.333e-02   2.259 0.023854 *  
Gendermale                -3.800e-01  2.498e-01  -1.521 0.128141    
Hypertension.compositeyes -2.389e-01  3.670e-01  -0.651 0.515128    
DiabetesStatusDiabetes    -7.154e-02  2.537e-01  -0.282 0.777963    
SmokerCurrentyes           1.609e-01  2.376e-01   0.677 0.498292    
Med.Statin.LLDyes         -2.106e-01  2.771e-01  -0.760 0.447143    
Med.all.antiplateletyes   -8.279e-01  4.507e-01  -1.837 0.066229 .  
GFR_MDRD                   5.094e-03  5.859e-03   0.869 0.384626    
BMI                       -5.086e-03  3.024e-02  -0.168 0.866441    
CAD_history               -2.107e-01  2.317e-01  -0.909 0.363109    
Stroke_history             1.078e+00  2.873e-01   3.752 0.000175 ***
Peripheral.interv         -5.651e-01  2.460e-01  -2.297 0.021601 *  
stenose50-70%             -1.349e+01  1.142e+03  -0.012 0.990570    
stenose70-90%             -1.461e+01  1.142e+03  -0.013 0.989789    
stenose90-99%             -1.445e+01  1.142e+03  -0.013 0.989902    
stenose100% (Occlusion)   -2.736e-01  1.393e+03   0.000 0.999843    
stenose50-99%             -1.670e+01  1.142e+03  -0.015 0.988331    
stenose70-99%             -1.888e-01  1.332e+03   0.000 0.999887    
IL6_pg_ug_2015_LN          1.301e-01  7.939e-02   1.639 0.101187    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 687.36  on 975  degrees of freedom
Residual deviance: 624.01  on 955  degrees of freedom
AIC: 666.01

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.279855 
Standard error............: 0.108675 
Odds ratio (effect size)..: 0.756 
Lower 95% CI..............: 0.611 
Upper 95% CI..............: 0.935 
Z-value...................: -2.575155 
P-value...................: 0.0100195 
Hosmer and Lemeshow r^2...: 0.092164 
Cox and Snell r^2.........: 0.062846 
Nagelkerke's pseudo r^2...: 0.124318 
Sample size of AE DB......: 2388 
Sample size of model......: 976 
Missing data %............: 59.12898 

Analysis of MCP1_pg_ug_2015_LN.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Med.all.antiplatelet + GFR_MDRD + Stroke_history + 
    Peripheral.interv + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age               Gendermale  Med.all.antiplateletyes  
              15.571730                 0.136390                 0.028145                -0.459467                -0.915731  
               GFR_MDRD           Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%  
               0.008321                 1.146387                -0.669062               -13.779325               -14.925585  
          stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
             -14.736397                -0.347463               -17.087502                -0.329992  

Degrees of Freedom: 1007 Total (i.e. Null);  994 Residual
Null Deviance:      707.4 
Residual Deviance: 648.6    AIC: 676.6

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_LN, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9070   0.2640   0.3966   0.5542   1.1087  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.566e+01  1.029e+03   0.015 0.987849    
currentDF[, PROTEIN]       1.355e-01  8.956e-02   1.513 0.130306    
Age                        3.141e-02  1.298e-02   2.420 0.015530 *  
Gendermale                -4.097e-01  2.445e-01  -1.675 0.093857 .  
Hypertension.compositeyes -2.809e-01  3.657e-01  -0.768 0.442438    
DiabetesStatusDiabetes    -2.837e-03  2.509e-01  -0.011 0.990980    
SmokerCurrentyes           1.636e-01  2.333e-01   0.701 0.483158    
Med.Statin.LLDyes         -1.508e-01  2.740e-01  -0.550 0.582051    
Med.all.antiplateletyes   -9.121e-01  4.501e-01  -2.026 0.042717 *  
GFR_MDRD                   7.303e-03  5.673e-03   1.287 0.197982    
BMI                        2.607e-04  2.867e-02   0.009 0.992745    
CAD_history               -1.829e-01  2.266e-01  -0.807 0.419512    
Stroke_history             1.109e+00  2.864e-01   3.873 0.000107 ***
Peripheral.interv         -6.424e-01  2.407e-01  -2.669 0.007611 ** 
stenose50-70%             -1.370e+01  1.029e+03  -0.013 0.989373    
stenose70-90%             -1.489e+01  1.029e+03  -0.014 0.988451    
stenose90-99%             -1.470e+01  1.029e+03  -0.014 0.988593    
stenose100% (Occlusion)   -3.751e-01  1.311e+03   0.000 0.999772    
stenose50-99%             -1.704e+01  1.029e+03  -0.017 0.986781    
stenose70-99%             -2.841e-01  1.236e+03   0.000 0.999817    
IL6_pg_ug_2015_LN         -1.685e-02  8.271e-02  -0.204 0.838531    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 707.39  on 1007  degrees of freedom
Residual deviance: 645.86  on  987  degrees of freedom
AIC: 687.86

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_LN ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_LN 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.1355 
Standard error............: 0.089563 
Odds ratio (effect size)..: 1.145 
Lower 95% CI..............: 0.961 
Upper 95% CI..............: 1.365 
Z-value...................: 1.512895 
P-value...................: 0.1303063 
Hosmer and Lemeshow r^2...: 0.08698 
Cox and Snell r^2.........: 0.059215 
Nagelkerke's pseudo r^2...: 0.117422 
Sample size of AE DB......: 2388 
Sample size of model......: 1008 
Missing data %............: 57.78894 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.


GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5rank) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }

Analysis of IL6_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes             Stroke_history          Peripheral.interv  
                   2.8970                    -1.4467                     1.5894                    -0.8629  

Degrees of Freedom: 370 Total (i.e. Null);  367 Residual
Null Deviance:      318.3 
Residual Deviance: 287  AIC: 295

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.7233   0.2313   0.3958   0.6502   1.3548  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.840e+01  1.654e+03   0.011 0.991125    
currentDF[, PROTEIN]       5.567e-02  1.702e-01   0.327 0.743551    
Age                        6.865e-03  1.952e-02   0.352 0.725052    
Gendermale                 2.965e-02  3.434e-01   0.086 0.931207    
Hypertension.compositeyes -1.535e+00  7.735e-01  -1.985 0.047130 *  
DiabetesStatusDiabetes     2.731e-02  3.801e-01   0.072 0.942721    
SmokerCurrentyes          -2.411e-01  3.319e-01  -0.726 0.467620    
Med.Statin.LLDyes         -3.834e-01  3.892e-01  -0.985 0.324634    
Med.all.antiplateletyes   -8.233e-01  6.737e-01  -1.222 0.221727    
GFR_MDRD                   3.604e-03  9.165e-03   0.393 0.694154    
BMI                        1.329e-03  4.227e-02   0.031 0.974915    
CAD_history               -2.050e-02  3.402e-01  -0.060 0.951956    
Stroke_history             1.614e+00  4.619e-01   3.494 0.000476 ***
Peripheral.interv         -8.466e-01  3.480e-01  -2.433 0.014983 *  
stenose50-70%             -1.424e+01  1.654e+03  -0.009 0.993130    
stenose70-90%             -1.538e+01  1.654e+03  -0.009 0.992582    
stenose90-99%             -1.486e+01  1.654e+03  -0.009 0.992833    
stenose100% (Occlusion)   -1.410e+00  1.991e+03  -0.001 0.999435    
IL6_pg_ug_2015_rank        9.545e-02  1.578e-01   0.605 0.545359    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 318.3  on 370  degrees of freedom
Residual deviance: 277.7  on 352  degrees of freedom
AIC: 315.7

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.055668 
Standard error............: 0.170159 
Odds ratio (effect size)..: 1.057 
Lower 95% CI..............: 0.757 
Upper 95% CI..............: 1.476 
Z-value...................: 0.327155 
P-value...................: 0.7435508 
Hosmer and Lemeshow r^2...: 0.127539 
Cox and Snell r^2.........: 0.103646 
Nagelkerke's pseudo r^2...: 0.179953 
Sample size of AE DB......: 2388 
Sample size of model......: 371 
Missing data %............: 84.46399 

Analysis of MCP1_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Hypertension.composite + 
    Med.Statin.LLD + Stroke_history + Peripheral.interv, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
              (Intercept)  Hypertension.compositeyes          Med.Statin.LLDyes             Stroke_history          Peripheral.interv  
                   3.4064                    -1.4720                    -0.5627                     1.6787                    -0.9431  

Degrees of Freedom: 394 Total (i.e. Null);  390 Residual
Null Deviance:      326 
Residual Deviance: 287.9    AIC: 297.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8502   0.2019   0.3580   0.6163   1.3684  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.855e+01  1.631e+03   0.011 0.990925    
currentDF[, PROTEIN]       2.259e-01  1.705e-01   1.325 0.185209    
Age                        1.073e-02  1.956e-02   0.549 0.583218    
Gendermale                -3.045e-02  3.460e-01  -0.088 0.929859    
Hypertension.compositeyes -1.639e+00  7.745e-01  -2.116 0.034335 *  
DiabetesStatusDiabetes     5.711e-03  3.765e-01   0.015 0.987899    
SmokerCurrentyes          -2.974e-01  3.337e-01  -0.891 0.372744    
Med.Statin.LLDyes         -4.032e-01  4.024e-01  -1.002 0.316352    
Med.all.antiplateletyes   -1.040e+00  6.833e-01  -1.522 0.128006    
GFR_MDRD                   2.505e-03  9.230e-03   0.271 0.786073    
BMI                        8.869e-03  4.176e-02   0.212 0.831806    
CAD_history               -1.018e-01  3.459e-01  -0.294 0.768495    
Stroke_history             1.677e+00  4.631e-01   3.622 0.000293 ***
Peripheral.interv         -9.896e-01  3.549e-01  -2.788 0.005304 ** 
stenose50-70%             -1.405e+01  1.631e+03  -0.009 0.993125    
stenose70-90%             -1.546e+01  1.631e+03  -0.009 0.992438    
stenose90-99%             -1.489e+01  1.631e+03  -0.009 0.992716    
stenose100% (Occlusion)   -1.435e+00  1.960e+03  -0.001 0.999416    
IL6_pg_ug_2015_rank        5.195e-02  1.594e-01   0.326 0.744452    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 326.03  on 394  degrees of freedom
Residual deviance: 277.58  on 376  degrees of freedom
AIC: 315.58

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.225877 
Standard error............: 0.170488 
Odds ratio (effect size)..: 1.253 
Lower 95% CI..............: 0.897 
Upper 95% CI..............: 1.751 
Z-value...................: 1.324886 
P-value...................: 0.1852089 
Hosmer and Lemeshow r^2...: 0.148606 
Cox and Snell r^2.........: 0.115435 
Nagelkerke's pseudo r^2...: 0.205422 
Sample size of AE DB......: 2388 
Sample size of model......: 395 
Missing data %............: 83.45896 

Analysis of IL6_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender + 
    Med.all.antiplatelet + GFR_MDRD + Stroke_history + Peripheral.interv + 
    stenose, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)                      Age               Gendermale  Med.all.antiplateletyes                 GFR_MDRD  
              15.387606                 0.027762                -0.453161                -0.916880                 0.008104  
         Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%            stenose90-99%  
               1.166727                -0.673292               -13.748714               -14.870639               -14.701591  
stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
              -0.331218               -16.941956                -0.167408  

Degrees of Freedom: 1008 Total (i.e. Null);  996 Residual
Null Deviance:      707.6 
Residual Deviance: 652.1    AIC: 678.1

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.8197   0.2654   0.4021   0.5591   0.9812  

Coefficients: (1 not defined because of singularities)
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.553e+01  1.027e+03   0.015 0.987936    
currentDF[, PROTEIN]       6.163e-02  1.053e-01   0.585 0.558336    
Age                        3.155e-02  1.295e-02   2.436 0.014857 *  
Gendermale                -4.004e-01  2.443e-01  -1.639 0.101287    
Hypertension.compositeyes -2.983e-01  3.648e-01  -0.818 0.413500    
DiabetesStatusDiabetes    -8.036e-03  2.508e-01  -0.032 0.974442    
SmokerCurrentyes           1.551e-01  2.332e-01   0.665 0.505947    
Med.Statin.LLDyes         -1.778e-01  2.733e-01  -0.651 0.515164    
Med.all.antiplateletyes   -9.050e-01  4.485e-01  -2.018 0.043614 *  
GFR_MDRD                   7.277e-03  5.677e-03   1.282 0.199907    
BMI                        9.863e-04  2.892e-02   0.034 0.972798    
CAD_history               -1.788e-01  2.262e-01  -0.790 0.429472    
Stroke_history             1.108e+00  2.862e-01   3.870 0.000109 ***
Peripheral.interv         -6.265e-01  2.400e-01  -2.611 0.009031 ** 
stenose50-70%             -1.368e+01  1.027e+03  -0.013 0.989371    
stenose70-90%             -1.486e+01  1.027e+03  -0.014 0.988453    
stenose90-99%             -1.469e+01  1.027e+03  -0.014 0.988582    
stenose100% (Occlusion)   -4.303e-01  1.306e+03   0.000 0.999737    
stenose50-99%             -1.691e+01  1.027e+03  -0.016 0.986859    
stenose70-99%             -1.372e-01  1.233e+03   0.000 0.999911    
IL6_pg_ug_2015_rank               NA         NA      NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 707.63  on 1008  degrees of freedom
Residual deviance: 648.64  on  989  degrees of freedom
AIC: 688.64

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.061634 
Standard error............: 0.1053 
Odds ratio (effect size)..: 1.064 
Lower 95% CI..............: 0.865 
Upper 95% CI..............: 1.307 
Z-value...................: 0.585315 
P-value...................: 0.5583357 
Hosmer and Lemeshow r^2...: 0.083364 
Cox and Snell r^2.........: 0.056789 
Nagelkerke's pseudo r^2...: 0.11266 
Sample size of AE DB......: 2388 
Sample size of model......: 1009 
Missing data %............: 57.74707 

Analysis of IL6R_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Med.all.antiplatelet + Stroke_history + Peripheral.interv + 
    IL6_pg_ug_2015_rank, family = binomial(link = "logit"), data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age               Gendermale  Med.all.antiplateletyes  
                 1.3871                  -0.2676                   0.0228                  -0.3736                  -0.7581  
         Stroke_history        Peripheral.interv      IL6_pg_ug_2015_rank  
                 1.0967                  -0.5702                   0.1770  

Degrees of Freedom: 976 Total (i.e. Null);  969 Residual
Null Deviance:      687.6 
Residual Deviance: 642  AIC: 658

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.7434   0.2654   0.3984   0.5457   1.0557  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.564e+01  1.146e+03   0.014 0.989110    
currentDF[, PROTEIN]      -2.501e-01  1.134e-01  -2.206 0.027368 *  
Age                        3.026e-02  1.330e-02   2.275 0.022891 *  
Gendermale                -3.735e-01  2.495e-01  -1.497 0.134481    
Hypertension.compositeyes -2.396e-01  3.668e-01  -0.653 0.513499    
DiabetesStatusDiabetes    -6.182e-02  2.532e-01  -0.244 0.807128    
SmokerCurrentyes           1.670e-01  2.372e-01   0.704 0.481234    
Med.Statin.LLDyes         -2.058e-01  2.771e-01  -0.743 0.457665    
Med.all.antiplateletyes   -8.375e-01  4.504e-01  -1.859 0.062968 .  
GFR_MDRD                   5.091e-03  5.857e-03   0.869 0.384726    
BMI                       -4.588e-03  3.030e-02  -0.151 0.879620    
CAD_history               -2.014e-01  2.313e-01  -0.871 0.383855    
Stroke_history             1.077e+00  2.871e-01   3.752 0.000175 ***
Peripheral.interv         -5.706e-01  2.460e-01  -2.320 0.020362 *  
stenose50-70%             -1.354e+01  1.146e+03  -0.012 0.990573    
stenose70-90%             -1.466e+01  1.146e+03  -0.013 0.989793    
stenose90-99%             -1.451e+01  1.146e+03  -0.013 0.989900    
stenose100% (Occlusion)   -3.044e-01  1.398e+03   0.000 0.999826    
stenose50-99%             -1.678e+01  1.146e+03  -0.015 0.988317    
stenose70-99%             -1.591e-01  1.336e+03   0.000 0.999905    
IL6_pg_ug_2015_rank        1.666e-01  1.149e-01   1.450 0.146970    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 687.60  on 976  degrees of freedom
Residual deviance: 626.44  on 956  degrees of freedom
AIC: 668.44

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' IL6R_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: IL6R_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: -0.250127 
Standard error............: 0.113373 
Odds ratio (effect size)..: 0.779 
Lower 95% CI..............: 0.624 
Upper 95% CI..............: 0.972 
Z-value...................: -2.206227 
P-value...................: 0.02736813 
Hosmer and Lemeshow r^2...: 0.088951 
Cox and Snell r^2.........: 0.060684 
Nagelkerke's pseudo r^2...: 0.120096 
Sample size of AE DB......: 2388 
Sample size of model......: 977 
Missing data %............: 59.0871 

Analysis of MCP1_pg_ug_2015_rank.

- processing AsymptSympt


Call:  glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Med.all.antiplatelet + GFR_MDRD + Stroke_history + 
    Peripheral.interv + stenose, family = binomial(link = "logit"), 
    data = currentDF)

Coefficients:
            (Intercept)     currentDF[, PROTEIN]                      Age               Gendermale  Med.all.antiplateletyes  
              15.413005                 0.201735                 0.028292                -0.458713                -0.916140  
               GFR_MDRD           Stroke_history        Peripheral.interv            stenose50-70%            stenose70-90%  
               0.008298                 1.144339                -0.666402               -13.789340               -14.939503  
          stenose90-99%  stenose100% (Occlusion)            stenose50-99%            stenose70-99%  
             -14.747482                -0.356754               -17.110247                -0.362039  

Degrees of Freedom: 1007 Total (i.e. Null);  994 Residual
Null Deviance:      707.4 
Residual Deviance: 647.9    AIC: 675.9

Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, family = binomial(link = "logit"), 
    data = currentDF)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9165   0.2626   0.3994   0.5531   1.1042  

Coefficients:
                            Estimate Std. Error z value Pr(>|z|)    
(Intercept)                1.556e+01  1.028e+03   0.015  0.98791    
currentDF[, PROTEIN]       2.053e-01  1.190e-01   1.726  0.08434 .  
Age                        3.153e-02  1.298e-02   2.429  0.01516 *  
Gendermale                -4.083e-01  2.445e-01  -1.670  0.09493 .  
Hypertension.compositeyes -2.765e-01  3.659e-01  -0.756  0.44980    
DiabetesStatusDiabetes    -5.170e-03  2.510e-01  -0.021  0.98357    
SmokerCurrentyes           1.651e-01  2.335e-01   0.707  0.47941    
Med.Statin.LLDyes         -1.524e-01  2.741e-01  -0.556  0.57839    
Med.all.antiplateletyes   -9.139e-01  4.503e-01  -2.029  0.04241 *  
GFR_MDRD                   7.258e-03  5.677e-03   1.278  0.20109    
BMI                        1.501e-04  2.864e-02   0.005  0.99582    
CAD_history               -1.852e-01  2.266e-01  -0.817  0.41384    
Stroke_history             1.108e+00  2.864e-01   3.868  0.00011 ***
Peripheral.interv         -6.415e-01  2.408e-01  -2.664  0.00773 ** 
stenose50-70%             -1.371e+01  1.028e+03  -0.013  0.98936    
stenose70-90%             -1.490e+01  1.028e+03  -0.015  0.98843    
stenose90-99%             -1.471e+01  1.028e+03  -0.014  0.98858    
stenose100% (Occlusion)   -3.816e-01  1.310e+03   0.000  0.99977    
stenose50-99%             -1.707e+01  1.028e+03  -0.017  0.98675    
stenose70-99%             -3.171e-01  1.235e+03   0.000  0.99980    
IL6_pg_ug_2015_rank       -3.253e-02  1.209e-01  -0.269  0.78795    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

    Null deviance: 707.39  on 1007  degrees of freedom
Residual deviance: 645.10  on  987  degrees of freedom
AIC: 687.1

Number of Fisher Scoring iterations: 15

Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA 
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank 
Trait/outcome.............: AsymptSympt 
Effect size...............: 0.205327 
Standard error............: 0.118957 
Odds ratio (effect size)..: 1.228 
Lower 95% CI..............: 0.973 
Upper 95% CI..............: 1.55 
Z-value...................: 1.726055 
P-value...................: 0.08433759 
Hosmer and Lemeshow r^2...: 0.088063 
Cox and Snell r^2.........: 0.05993 
Nagelkerke's pseudo r^2...: 0.118838 
Sample size of AE DB......: 2388 
Sample size of model......: 1008 
Missing data %............: 57.78894 
cat("Edit the column names...\n")
Edit the column names...
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
Correct the variable types...
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
Writing results to Excel-file...
### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
Removing intermediate files...
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque and serum MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period.

The primary outcome is defined as “a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death”, i.e. major adverse cardiovascular events (MACE). Variable: epmajor.3years, these include: - myocardial infarction (MI) - cerebral infarction (CVA/stroke) - cardiovascular death (exact cause to be investigated) - cerebral bleeding (CVA/stroke) - fatal myocardial infarction (MI) - fatal cerebral infarction - fatal cerebral bleeding - sudden death - fatal heart failure - fatal aneurysm rupture - other cardiovascular death..

The secondary outcomes will be

  • incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: epstroke.3years, these include:
    • cerebral infarction (CVA/stroke)
    • cerebral bleeding (CVA/stroke)
    • fatal cerebral infarction
    • fatal cerebral bleeding.
  • incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: epcoronary.3years, these include:
    • myocardial infarction (MI)
    • coronary angioplasty (PCI/PTCA)
    • cardiovascular death (exact cause to be investigated)
    • coronary bypass (CABG)
    • fatal myocardial infarction (MI)
    • sudden death.
  • cardiovascular death - variable: epcvdeath.3years, these include:
    • cardiovascular death (exact cause to be investigated)
    • fatal myocardial infarction (MI)
    • fatal cerebral infarction
    • fatal cerebral bleeding
    • sudden death
    • fatal heart failure
    • fatal aneurysm rupture
    • other cardiovascular death..

Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.

# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
* Creating function to summarize Cox regression and prepare container for results.
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
  cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(protein, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", protein, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
* Check the cases per event type - for sanity.
for (events in endpoints){
  print(paste0("Printing the summary of: ",events))
  print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}
[1] "Printing the summary of: epmajor.3years"
 epmajor.3years  
 Min.   :0.0000  
 1st Qu.:0.0000  
 Median :0.0000  
 Mean   :0.1145  
 3rd Qu.:0.0000  
 Max.   :1.0000  
 NA's   :127     

   0    1 
2002  259 
[1] "Printing the summary of: epstroke.3years"
 epstroke.3years  
 Min.   :0.00000  
 1st Qu.:0.00000  
 Median :0.00000  
 Mean   :0.05659  
 3rd Qu.:0.00000  
 Max.   :1.00000  
 NA's   :126      

   0    1 
2134  128 
[1] "Printing the summary of: epcoronary.3years"
 epcoronary.3years
 Min.   :0.00000  
 1st Qu.:0.00000  
 Median :0.00000  
 Mean   :0.07825  
 3rd Qu.:0.00000  
 Max.   :1.00000  
 NA's   :126      

   0    1 
2085  177 
[1] "Printing the summary of: epcvdeath.3years"
 epcvdeath.3years 
 Min.   :0.00000  
 1st Qu.:0.00000  
 Median :0.00000  
 Mean   :0.03892  
 3rd Qu.:0.00000  
 Max.   :1.00000  
 NA's   :127      

   0    1 
2173   88 
cat("* Check distribution of events over time - for sanity.")
* Check distribution of events over time - for sanity.
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}
[1] "Printing the summary of: ep_major_t_3years"
 ep_major_t_3years
 Min.   :0.000    
 1st Qu.:2.770    
 Median :3.000    
 Mean   :2.585    
 3rd Qu.:3.000    
 Max.   :3.000    
 NA's   :129      
[1] "Printing the summary of: ep_stroke_t_3years"
 ep_stroke_t_3years
 Min.   :0.000     
 1st Qu.:2.890     
 Median :3.000     
 Mean   :2.636     
 3rd Qu.:3.000     
 Max.   :3.000     
 NA's   :129       
[1] "Printing the summary of: ep_coronary_t_3years"
 ep_coronary_t_3years
 Min.   :0.000       
 1st Qu.:2.851       
 Median :3.000       
 Mean   :2.637       
 3rd Qu.:3.000       
 Max.   :3.000       
 NA's   :129         
[1] "Printing the summary of: ep_cvdeath_t_3years"
 ep_cvdeath_t_3years
 Min.   :0.00274    
 1st Qu.:2.91781    
 Median :3.00000    
 Mean   :2.72214    
 3rd Qu.:3.00000    
 Max.   :3.00000    
 NA's   :129        
for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}
[1] "Printing the distribution of: ep_major_t_3years"
[1] "Printing the distribution of: ep_stroke_t_3years"
[1] "Printing the distribution of: ep_coronary_t_3years"
[1] "Printing the distribution of: ep_cvdeath_t_3years"

Cox regressions

Let’s perform the actual Cox-regressions. We will apply a couple of models:

  • Model 1: adjusted for age and sex
  • Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
  • Model 3: same to model 2, with additional adjustments for circulating CRP levels
  • Model 4: same to model 2 with additional adjustment for IL6 levels in the plaque

MODEL 1

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}
* Analyzing the effect of plaque proteins on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 522, number of events= 70 
   (1866 observations deleted due to missingness)

                                                             coef exp(coef) se(coef)     z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 0.15269   1.16496  0.23974 0.637  0.52420   
Age                                                       0.03853   1.03928  0.01471 2.620  0.00879 **
Gendermale                                                0.78339   2.18888  0.32848 2.385  0.01708 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694]     1.165     0.8584    0.7282     1.864
Age                                                           1.039     0.9622    1.0098     1.070
Gendermale                                                    2.189     0.4569    1.1498     4.167

Concordance= 0.632  (se = 0.036 )
Likelihood ratio test= 14.17  on 3 df,   p=0.003
Wald test            = 12.77  on 3 df,   p=0.005
Score (logrank) test = 13.09  on 3 df,   p=0.004


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: IL6_rank 
Effect size...............: 0.152688 
Standard error............: 0.239742 
Odds ratio (effect size)..: 1.165 
Lower 95% CI..............: 0.728 
Upper 95% CI..............: 1.864 
T-value...................: 0.636883 
P-value...................: 0.524201 
Sample size in model......: 522 
Number of events..........: 70 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 558, number of events= 72 
   (1830 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -0.20048   0.81834  0.23746 -0.844   0.3985  
Age                                                        0.03070   1.03117  0.01446  2.123   0.0338 *
Gendermale                                                 0.81021   2.24838  0.32813  2.469   0.0135 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635]    0.8183     1.2220    0.5138     1.303
Age                                                          1.0312     0.9698    1.0024     1.061
Gendermale                                                   2.2484     0.4448    1.1819     4.277

Concordance= 0.62  (se = 0.035 )
Likelihood ratio test= 12.85  on 3 df,   p=0.005
Wald test            = 11.56  on 3 df,   p=0.009
Score (logrank) test = 11.89  on 3 df,   p=0.008


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.200476 
Standard error............: 0.237458 
Odds ratio (effect size)..: 0.818 
Lower 95% CI..............: 0.514 
Upper 95% CI..............: 1.303 
T-value...................: -0.844258 
P-value...................: 0.3985254 
Sample size in model......: 558 
Number of events..........: 72 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1141, number of events= 133 
   (1247 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] -0.03418   0.96640  0.17344 -0.197 0.843795    
Age                                                        0.03487   1.03549  0.01013  3.441 0.000579 ***
Gendermale                                                 0.44653   1.56287  0.21004  2.126 0.033515 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061]    0.9664     1.0348    0.6879     1.358
Age                                                          1.0355     0.9657    1.0151     1.056
Gendermale                                                   1.5629     0.6398    1.0355     2.359

Concordance= 0.597  (se = 0.026 )
Likelihood ratio test= 17.19  on 3 df,   p=6e-04
Wald test            = 16.13  on 3 df,   p=0.001
Score (logrank) test = 16.22  on 3 df,   p=0.001


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: -0.034175 
Standard error............: 0.173443 
Odds ratio (effect size)..: 0.966 
Lower 95% CI..............: 0.688 
Upper 95% CI..............: 1.358 
T-value...................: -0.197041 
P-value...................: 0.8437952 
Sample size in model......: 1141 
Number of events..........: 133 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1143, number of events= 137 
   (1245 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)     z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 0.448497  1.565956 0.174488 2.570 0.010159 *  
Age                                                       0.033432  1.033997 0.009989 3.347 0.000817 ***
Gendermale                                                0.335403  1.398504 0.201961 1.661 0.096766 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]     1.566     0.6386    1.1124     2.204
Age                                                           1.034     0.9671    1.0140     1.054
Gendermale                                                    1.399     0.7150    0.9414     2.078

Concordance= 0.607  (se = 0.024 )
Likelihood ratio test= 20.48  on 3 df,   p=1e-04
Wald test            = 19.65  on 3 df,   p=2e-04
Score (logrank) test = 19.73  on 3 df,   p=2e-04


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.448497 
Standard error............: 0.174488 
Odds ratio (effect size)..: 1.566 
Lower 95% CI..............: 1.112 
Upper 95% CI..............: 2.204 
T-value...................: 2.570364 
P-value...................: 0.01015916 
Sample size in model......: 1143 
Number of events..........: 137 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1187, number of events= 139 
   (1201 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)     z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 0.037312  1.038016 0.169989 0.219 0.826266    
Age                                                       0.033060  1.033613 0.009864 3.351 0.000804 ***
Gendermale                                                0.343970  1.410536 0.199674 1.723 0.084950 .  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148]     1.038     0.9634    0.7439     1.448
Age                                                           1.034     0.9675    1.0138     1.054
Gendermale                                                    1.411     0.7090    0.9537     2.086

Concordance= 0.587  (se = 0.025 )
Likelihood ratio test= 14.94  on 3 df,   p=0.002
Wald test            = 14.19  on 3 df,   p=0.003
Score (logrank) test = 14.26  on 3 df,   p=0.003


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.037312 
Standard error............: 0.169989 
Odds ratio (effect size)..: 1.038 
Lower 95% CI..............: 0.744 
Upper 95% CI..............: 1.448 
T-value...................: 0.219494 
P-value...................: 0.8262655 
Sample size in model......: 1187 
Number of events..........: 139 
* Analyzing the effect of plaque proteins on [epstroke.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 522, number of events= 37 
   (1866 observations deleted due to missingness)

                                                             coef exp(coef) se(coef)     z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 0.07624   1.07922  0.32935 0.231    0.817
Age                                                       0.02809   1.02849  0.01976 1.422    0.155
Gendermale                                                0.20807   1.23130  0.38325 0.543    0.587

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694]     1.079     0.9266    0.5659     2.058
Age                                                           1.028     0.9723    0.9894     1.069
Gendermale                                                    1.231     0.8122    0.5809     2.610

Concordance= 0.569  (se = 0.051 )
Likelihood ratio test= 2.43  on 3 df,   p=0.5
Wald test            = 2.35  on 3 df,   p=0.5
Score (logrank) test = 2.35  on 3 df,   p=0.5


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: IL6_rank 
Effect size...............: 0.076241 
Standard error............: 0.329345 
Odds ratio (effect size)..: 1.079 
Lower 95% CI..............: 0.566 
Upper 95% CI..............: 2.058 
T-value...................: 0.231493 
P-value...................: 0.8169321 
Sample size in model......: 522 
Number of events..........: 37 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 558, number of events= 38 
   (1830 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)      z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -0.33054   0.71854  0.32977 -1.002    0.316
Age                                                        0.01603   1.01615  0.01930  0.830    0.406
Gendermale                                                 0.26127   1.29858  0.38266  0.683    0.495

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635]    0.7185     1.3917    0.3765     1.371
Age                                                          1.0162     0.9841    0.9784     1.055
Gendermale                                                   1.2986     0.7701    0.6134     2.749

Concordance= 0.558  (se = 0.044 )
Likelihood ratio test= 2.22  on 3 df,   p=0.5
Wald test            = 2.2  on 3 df,   p=0.5
Score (logrank) test = 2.21  on 3 df,   p=0.5


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.330541 
Standard error............: 0.329767 
Odds ratio (effect size)..: 0.719 
Lower 95% CI..............: 0.376 
Upper 95% CI..............: 1.371 
T-value...................: -1.002346 
P-value...................: 0.3161763 
Sample size in model......: 558 
Number of events..........: 38 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1141, number of events= 69 
   (1247 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] -0.17128   0.84258  0.24143 -0.709  0.47805   
Age                                                        0.03832   1.03907  0.01407  2.724  0.00645 **
Gendermale                                                 0.12792   1.13646  0.26956  0.475  0.63511   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061]    0.8426     1.1868    0.5249     1.352
Age                                                          1.0391     0.9624    1.0108     1.068
Gendermale                                                   1.1365     0.8799    0.6700     1.928

Concordance= 0.601  (se = 0.035 )
Likelihood ratio test= 8.42  on 3 df,   p=0.04
Wald test            = 8.05  on 3 df,   p=0.05
Score (logrank) test = 8.1  on 3 df,   p=0.04


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: -0.171284 
Standard error............: 0.241434 
Odds ratio (effect size)..: 0.843 
Lower 95% CI..............: 0.525 
Upper 95% CI..............: 1.352 
T-value...................: -0.709446 
P-value...................: 0.4780475 
Sample size in model......: 1141 
Number of events..........: 69 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1143, number of events= 72 
   (1245 observations deleted due to missingness)

                                                             coef exp(coef) se(coef)     z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 0.37431   1.45399  0.23932 1.564   0.1178  
Age                                                       0.03294   1.03349  0.01373 2.400   0.0164 *
Gendermale                                                0.09556   1.10027  0.26316 0.363   0.7165  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]     1.454     0.6878    0.9096     2.324
Age                                                           1.033     0.9676    1.0061     1.062
Gendermale                                                    1.100     0.9089    0.6569     1.843

Concordance= 0.593  (se = 0.034 )
Likelihood ratio test= 8.25  on 3 df,   p=0.04
Wald test            = 7.97  on 3 df,   p=0.05
Score (logrank) test = 8  on 3 df,   p=0.05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.374314 
Standard error............: 0.239317 
Odds ratio (effect size)..: 1.454 
Lower 95% CI..............: 0.91 
Upper 95% CI..............: 2.324 
T-value...................: 1.564093 
P-value...................: 0.1177957 
Sample size in model......: 1143 
Number of events..........: 72 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1187, number of events= 73 
   (1201 observations deleted due to missingness)

                                                             coef exp(coef) se(coef)     z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 0.13635   1.14608  0.23507 0.580   0.5619  
Age                                                       0.03393   1.03451  0.01354 2.505   0.0122 *
Gendermale                                                0.07048   1.07302  0.25901 0.272   0.7855  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148]     1.146     0.8725    0.7230     1.817
Age                                                           1.035     0.9666    1.0074     1.062
Gendermale                                                    1.073     0.9319    0.6459     1.783

Concordance= 0.595  (se = 0.033 )
Likelihood ratio test= 6.98  on 3 df,   p=0.07
Wald test            = 6.75  on 3 df,   p=0.08
Score (logrank) test = 6.78  on 3 df,   p=0.08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.13635 
Standard error............: 0.235072 
Odds ratio (effect size)..: 1.146 
Lower 95% CI..............: 0.723 
Upper 95% CI..............: 1.817 
T-value...................: 0.580033 
P-value...................: 0.5618924 
Sample size in model......: 1187 
Number of events..........: 73 
* Analyzing the effect of plaque proteins on [epcoronary.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 522, number of events= 47 
   (1866 observations deleted due to missingness)

                                                             coef exp(coef) se(coef)     z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 0.17371   1.18970  0.29265 0.594   0.5528  
Age                                                       0.03896   1.03973  0.01792 2.175   0.0297 *
Gendermale                                                1.01973   2.77244  0.43719 2.332   0.0197 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694]     1.190     0.8405    0.6704     2.111
Age                                                           1.040     0.9618    1.0039     1.077
Gendermale                                                    2.772     0.3607    1.1769     6.531

Concordance= 0.646  (se = 0.038 )
Likelihood ratio test= 12.15  on 3 df,   p=0.007
Wald test            = 10.44  on 3 df,   p=0.02
Score (logrank) test = 10.96  on 3 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: IL6_rank 
Effect size...............: 0.173705 
Standard error............: 0.292653 
Odds ratio (effect size)..: 1.19 
Lower 95% CI..............: 0.67 
Upper 95% CI..............: 2.111 
T-value...................: 0.593554 
P-value...................: 0.5528105 
Sample size in model......: 522 
Number of events..........: 47 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 558, number of events= 47 
   (1830 observations deleted due to missingness)

                                                             coef exp(coef) se(coef)     z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 0.30417   1.35550  0.29625 1.027   0.3045  
Age                                                       0.03606   1.03672  0.01811 1.991   0.0464 *
Gendermale                                                0.79302   2.21007  0.41021 1.933   0.0532 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635]     1.356     0.7377    0.7585     2.423
Age                                                           1.037     0.9646    1.0006     1.074
Gendermale                                                    2.210     0.4525    0.9891     4.938

Concordance= 0.628  (se = 0.039 )
Likelihood ratio test= 9.85  on 3 df,   p=0.02
Wald test            = 8.75  on 3 df,   p=0.03
Score (logrank) test = 8.99  on 3 df,   p=0.03


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_rank 
Effect size...............: 0.304172 
Standard error............: 0.296253 
Odds ratio (effect size)..: 1.356 
Lower 95% CI..............: 0.758 
Upper 95% CI..............: 2.423 
T-value...................: 1.026729 
P-value...................: 0.3045479 
Sample size in model......: 558 
Number of events..........: 47 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1141, number of events= 89 
   (1247 observations deleted due to missingness)

                                                               coef exp(coef)  se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] -0.079260  0.923800  0.212158 -0.374  0.70871   
Age                                                        0.005236  1.005250  0.011952  0.438  0.66131   
Gendermale                                                 0.776579  2.174022  0.283231  2.742  0.00611 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061]    0.9238     1.0825    0.6095     1.400
Age                                                          1.0053     0.9948    0.9820     1.029
Gendermale                                                   2.1740     0.4600    1.2479     3.787

Concordance= 0.575  (se = 0.028 )
Likelihood ratio test= 9.22  on 3 df,   p=0.03
Wald test            = 7.88  on 3 df,   p=0.05
Score (logrank) test = 8.27  on 3 df,   p=0.04


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: -0.07926 
Standard error............: 0.212158 
Odds ratio (effect size)..: 0.924 
Lower 95% CI..............: 0.61 
Upper 95% CI..............: 1.4 
T-value...................: -0.373589 
P-value...................: 0.7087101 
Sample size in model......: 1141 
Number of events..........: 89 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1143, number of events= 90 
   (1245 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)     z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 0.382893  1.466522 0.214416 1.786   0.0741 .
Age                                                       0.007227  1.007253 0.011916 0.607   0.5442  
Gendermale                                                0.619255  1.857544 0.269355 2.299   0.0215 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]     1.467     0.6819    0.9633     2.233
Age                                                           1.007     0.9928    0.9840     1.031
Gendermale                                                    1.858     0.5383    1.0956     3.149

Concordance= 0.59  (se = 0.029 )
Likelihood ratio test= 9.52  on 3 df,   p=0.02
Wald test            = 8.77  on 3 df,   p=0.03
Score (logrank) test = 8.98  on 3 df,   p=0.03


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.382893 
Standard error............: 0.214416 
Odds ratio (effect size)..: 1.467 
Lower 95% CI..............: 0.963 
Upper 95% CI..............: 2.233 
T-value...................: 1.785754 
P-value...................: 0.0741391 
Sample size in model......: 1143 
Number of events..........: 90 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1187, number of events= 91 
   (1201 observations deleted due to missingness)

                                                               coef exp(coef)  se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] -0.172568  0.841501  0.210487 -0.820   0.4123  
Age                                                        0.006873  1.006897  0.011840  0.581   0.5616  
Gendermale                                                 0.671236  1.956655  0.269190  2.494   0.0126 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148]    0.8415     1.1884    0.5570     1.271
Age                                                          1.0069     0.9932    0.9838     1.031
Gendermale                                                   1.9567     0.5111    1.1545     3.316

Concordance= 0.577  (se = 0.03 )
Likelihood ratio test= 7.99  on 3 df,   p=0.05
Wald test            = 7.11  on 3 df,   p=0.07
Score (logrank) test = 7.34  on 3 df,   p=0.06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: -0.172568 
Standard error............: 0.210487 
Odds ratio (effect size)..: 0.842 
Lower 95% CI..............: 0.557 
Upper 95% CI..............: 1.271 
T-value...................: -0.819853 
P-value...................: 0.4122998 
Sample size in model......: 1187 
Number of events..........: 91 
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 522, number of events= 27 
   (1866 observations deleted due to missingness)

                                                             coef exp(coef) se(coef)     z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 0.12032   1.12786  0.38592 0.312   0.7552  
Age                                                       0.05430   1.05581  0.02434 2.231   0.0257 *
Gendermale                                                0.82329   2.27798  0.54178 1.520   0.1286  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694]     1.128     0.8866    0.5294     2.403
Age                                                           1.056     0.9471    1.0066     1.107
Gendermale                                                    2.278     0.4390    0.7877     6.587

Concordance= 0.665  (se = 0.059 )
Likelihood ratio test= 8.18  on 3 df,   p=0.04
Wald test            = 7.35  on 3 df,   p=0.06
Score (logrank) test = 7.53  on 3 df,   p=0.06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: IL6_rank 
Effect size...............: 0.120318 
Standard error............: 0.385919 
Odds ratio (effect size)..: 1.128 
Lower 95% CI..............: 0.529 
Upper 95% CI..............: 2.403 
T-value...................: 0.311771 
P-value...................: 0.7552146 
Sample size in model......: 522 
Number of events..........: 27 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 558, number of events= 27 
   (1830 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -0.05233   0.94902  0.38729 -0.135   0.8925  
Age                                                        0.05687   1.05852  0.02468  2.304   0.0212 *
Gendermale                                                 0.79435   2.21300  0.54234  1.465   0.1430  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635]     0.949     1.0537    0.4442     2.027
Age                                                           1.059     0.9447    1.0085     1.111
Gendermale                                                    2.213     0.4519    0.7644     6.407

Concordance= 0.667  (se = 0.059 )
Likelihood ratio test= 8.48  on 3 df,   p=0.04
Wald test            = 7.69  on 3 df,   p=0.05
Score (logrank) test = 7.85  on 3 df,   p=0.05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.052326 
Standard error............: 0.387295 
Odds ratio (effect size)..: 0.949 
Lower 95% CI..............: 0.444 
Upper 95% CI..............: 2.027 
T-value...................: -0.135107 
P-value...................: 0.8925272 
Sample size in model......: 558 
Number of events..........: 27 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1141, number of events= 45 
   (1247 observations deleted due to missingness)

                                                             coef exp(coef) se(coef)     z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 0.10096   1.10623  0.29884 0.338   0.7355    
Age                                                       0.08294   1.08648  0.01919 4.322 1.55e-05 ***
Gendermale                                                0.89258   2.44141  0.41182 2.167   0.0302 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061]     1.106     0.9040    0.6158     1.987
Age                                                           1.086     0.9204    1.0464     1.128
Gendermale                                                    2.441     0.4096    1.0892     5.472

Concordance= 0.706  (se = 0.037 )
Likelihood ratio test= 26.21  on 3 df,   p=9e-06
Wald test            = 22.65  on 3 df,   p=5e-05
Score (logrank) test = 23.27  on 3 df,   p=4e-05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: 0.100959 
Standard error............: 0.298838 
Odds ratio (effect size)..: 1.106 
Lower 95% CI..............: 0.616 
Upper 95% CI..............: 1.987 
T-value...................: 0.337838 
P-value...................: 0.7354853 
Sample size in model......: 1141 
Number of events..........: 45 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1143, number of events= 45 
   (1245 observations deleted due to missingness)

                                                             coef exp(coef) se(coef)     z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 0.77149   2.16299  0.31659 2.437   0.0148 *  
Age                                                       0.08479   1.08849  0.01926 4.403 1.07e-05 ***
Gendermale                                                0.87112   2.38958  0.41164 2.116   0.0343 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]     2.163     0.4623     1.163     4.023
Age                                                           1.088     0.9187     1.048     1.130
Gendermale                                                    2.390     0.4185     1.066     5.354

Concordance= 0.727  (se = 0.037 )
Likelihood ratio test= 31.83  on 3 df,   p=6e-07
Wald test            = 28.21  on 3 df,   p=3e-06
Score (logrank) test = 28.76  on 3 df,   p=3e-06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.77149 
Standard error............: 0.316594 
Odds ratio (effect size)..: 2.163 
Lower 95% CI..............: 1.163 
Upper 95% CI..............: 4.023 
T-value...................: 2.436846 
P-value...................: 0.01481598 
Sample size in model......: 1143 
Number of events..........: 45 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender, data = TEMP.DF)

  n= 1187, number of events= 45 
   (1201 observations deleted due to missingness)

                                                              coef exp(coef) se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] -0.11294   0.89320  0.29879 -0.378   0.7054    
Age                                                        0.08412   1.08776  0.01929  4.360  1.3e-05 ***
Gendermale                                                 0.90814   2.47970  0.41206  2.204   0.0275 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148]    0.8932     1.1196    0.4973     1.604
Age                                                          1.0878     0.9193    1.0474     1.130
Gendermale                                                   2.4797     0.4033    1.1057     5.561

Concordance= 0.709  (se = 0.036 )
Likelihood ratio test= 26.75  on 3 df,   p=7e-06
Wald test            = 23.06  on 3 df,   p=4e-05
Score (logrank) test = 23.7  on 3 df,   p=3e-05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: -0.112944 
Standard error............: 0.298793 
Odds ratio (effect size)..: 0.893 
Lower 95% CI..............: 0.497 
Upper 95% CI..............: 1.604 
T-value...................: -0.378 
P-value...................: 0.7054304 
Sample size in model......: 1187 
Number of events..........: 45 

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

MODEL 2

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}
* Analyzing the effect of plaque proteins on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 477, number of events= 64 
   (1911 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694]  1.309e-01  1.140e+00  2.580e-01  0.508  0.61176   
Age                                                        4.862e-02  1.050e+00  1.825e-02  2.664  0.00773 **
Gendermale                                                 6.880e-01  1.990e+00  3.396e-01  2.026  0.04279 * 
Hypertension.compositeno                                  -7.622e-01  4.666e-01  5.345e-01 -1.426  0.15387   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     7.270e-01  2.069e+00  2.896e-01  2.510  0.01207 * 
SmokerCurrentno                                           -6.648e-01  5.144e-01  2.676e-01 -2.484  0.01298 * 
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                           2.278e-01  1.256e+00  2.880e-01  0.791  0.42898   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     1.425e-01  1.153e+00  4.189e-01  0.340  0.73377   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -4.913e-03  9.951e-01  6.711e-03 -0.732  0.46411   
BMI                                                        5.332e-04  1.001e+00  3.520e-02  0.015  0.98792   
CAD_history                                                5.932e-01  1.810e+00  2.727e-01  2.176  0.02959 * 
Stroke_history                                             1.930e-01  1.213e+00  2.673e-01  0.722  0.47032   
Peripheral.interv                                          6.059e-02  1.062e+00  3.164e-01  0.191  0.84814   
stenose0-49%                                              -1.611e+01  1.011e-07  3.372e+03 -0.005  0.99619   
stenose50-70%                                             -1.333e+00  2.637e-01  1.461e+00 -0.912  0.36164   
stenose70-90%                                             -4.726e-01  6.234e-01  1.061e+00 -0.445  0.65609   
stenose90-99%                                             -7.412e-01  4.765e-01  1.069e+00 -0.694  0.48795   
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 1.140e+00  8.773e-01   0.68752    1.8899
Age                                                       1.050e+00  9.525e-01   1.01293    1.0881
Gendermale                                                1.990e+00  5.026e-01   1.02259    3.8717
Hypertension.compositeno                                  4.666e-01  2.143e+00   0.16368    1.3303
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    2.069e+00  4.834e-01   1.17271    3.6496
SmokerCurrentno                                           5.144e-01  1.944e+00   0.30442    0.8691
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.256e+00  7.963e-01   0.71413    2.2084
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.153e+00  8.672e-01   0.50733    2.6210
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.951e-01  1.005e+00   0.98210    1.0083
BMI                                                       1.001e+00  9.995e-01   0.93383    1.0720
CAD_history                                               1.810e+00  5.525e-01   1.06055    3.0885
Stroke_history                                            1.213e+00  8.245e-01   0.71822    2.0483
Peripheral.interv                                         1.062e+00  9.412e-01   0.57145    1.9754
stenose0-49%                                              1.011e-07  9.891e+06   0.00000       Inf
stenose50-70%                                             2.637e-01  3.792e+00   0.01505    4.6215
stenose70-90%                                             6.234e-01  1.604e+00   0.07787    4.9903
stenose90-99%                                             4.765e-01  2.098e+00   0.05868    3.8703
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.714  (se = 0.029 )
Likelihood ratio test= 37  on 17 df,   p=0.003
Wald test            = 33.02  on 17 df,   p=0.01
Score (logrank) test = 35.81  on 17 df,   p=0.005


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: IL6_rank 
Effect size...............: 0.130932 
Standard error............: 0.257961 
Odds ratio (effect size)..: 1.14 
Lower 95% CI..............: 0.688 
Upper 95% CI..............: 1.89 
T-value...................: 0.507564 
P-value...................: 0.6117595 
Sample size in model......: 477 
Number of events..........: 64 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 509, number of events= 66 
   (1879 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -3.049e-01  7.372e-01  2.532e-01 -1.204   0.2287  
Age                                                        3.571e-02  1.036e+00  1.769e-02  2.019   0.0435 *
Gendermale                                                 7.112e-01  2.036e+00  3.367e-01  2.112   0.0347 *
Hypertension.compositeno                                  -8.086e-01  4.455e-01  5.335e-01 -1.516   0.1296  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                     6.394e-01  1.895e+00  2.842e-01  2.250   0.0244 *
SmokerCurrentno                                           -5.756e-01  5.624e-01  2.650e-01 -2.172   0.0299 *
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA  
Med.Statin.LLDno                                           2.742e-01  1.315e+00  2.847e-01  0.963   0.3356  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     3.447e-03  1.003e+00  4.203e-01  0.008   0.9935  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -9.214e-03  9.908e-01  6.629e-03 -1.390   0.1646  
BMI                                                        1.203e-02  1.012e+00  3.398e-02  0.354   0.7234  
CAD_history                                                4.032e-01  1.497e+00  2.681e-01  1.504   0.1326  
Stroke_history                                             2.966e-01  1.345e+00  2.596e-01  1.142   0.2533  
Peripheral.interv                                          1.347e-01  1.144e+00  3.143e-01  0.429   0.6682  
stenose0-49%                                              -1.653e+01  6.613e-08  3.378e+03 -0.005   0.9961  
stenose50-70%                                             -1.817e+00  1.625e-01  1.455e+00 -1.249   0.2118  
stenose70-90%                                             -8.410e-01  4.313e-01  1.052e+00 -0.799   0.4240  
stenose90-99%                                             -1.061e+00  3.461e-01  1.061e+00 -1.000   0.3172  
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 7.372e-01  1.356e+00  0.448785    1.2111
Age                                                       1.036e+00  9.649e-01  1.001040    1.0729
Gendermale                                                2.036e+00  4.911e-01  1.052586    3.9395
Hypertension.compositeno                                  4.455e-01  2.245e+00  0.156588    1.2674
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.895e+00  5.276e-01  1.085923    3.3081
SmokerCurrentno                                           5.624e-01  1.778e+00  0.334542    0.9454
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.315e+00  7.602e-01  0.752853    2.2986
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.003e+00  9.966e-01  0.440301    2.2869
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.908e-01  1.009e+00  0.978038    1.0038
BMI                                                       1.012e+00  9.880e-01  0.946891    1.0818
CAD_history                                               1.497e+00  6.682e-01  0.884884    2.5311
Stroke_history                                            1.345e+00  7.434e-01  0.808757    2.2376
Peripheral.interv                                         1.144e+00  8.740e-01  0.617943    2.1187
stenose0-49%                                              6.613e-08  1.512e+07  0.000000       Inf
stenose50-70%                                             1.625e-01  6.155e+00  0.009373    2.8157
stenose70-90%                                             4.313e-01  2.319e+00  0.054876    3.3897
stenose90-99%                                             3.461e-01  2.889e+00  0.043283    2.7680
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.7  (se = 0.028 )
Likelihood ratio test= 35.01  on 17 df,   p=0.006
Wald test            = 31.02  on 17 df,   p=0.02
Score (logrank) test = 33.48  on 17 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.304862 
Standard error............: 0.253245 
Odds ratio (effect size)..: 0.737 
Lower 95% CI..............: 0.449 
Upper 95% CI..............: 1.211 
T-value...................: -1.203825 
P-value...................: 0.2286573 
Sample size in model......: 509 
Number of events..........: 66 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 1002, number of events= 114 
   (1386 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061]  9.097e-02  1.095e+00  1.899e-01  0.479 0.631870    
Age                                                        3.856e-02  1.039e+00  1.296e-02  2.975 0.002927 ** 
Gendermale                                                 5.730e-01  1.774e+00  2.310e-01  2.481 0.013107 *  
Hypertension.compositeno                                  -5.174e-01  5.961e-01  3.773e-01 -1.371 0.170285    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -4.403e-02  9.569e-01  2.233e-01 -0.197 0.843644    
SmokerCurrentno                                           -6.023e-01  5.475e-01  2.046e-01 -2.944 0.003240 ** 
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           3.369e-01  1.401e+00  2.175e-01  1.549 0.121332    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     4.026e-01  1.496e+00  2.586e-01  1.557 0.119528    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -1.899e-02  9.812e-01  4.977e-03 -3.815 0.000136 ***
BMI                                                        5.747e-02  1.059e+00  2.597e-02  2.213 0.026905 *  
CAD_history                                                1.411e-01  1.152e+00  2.032e-01  0.695 0.487257    
Stroke_history                                             4.304e-02  1.044e+00  2.028e-01  0.212 0.831900    
Peripheral.interv                                          6.318e-01  1.881e+00  2.183e-01  2.894 0.003802 ** 
stenose0-49%                                              -1.561e+01  1.659e-07  2.745e+03 -0.006 0.995462    
stenose50-70%                                             -8.519e-01  4.266e-01  8.692e-01 -0.980 0.326998    
stenose70-90%                                             -2.953e-01  7.443e-01  7.264e-01 -0.406 0.684395    
stenose90-99%                                             -2.722e-01  7.617e-01  7.236e-01 -0.376 0.706782    
stenose100% (Occlusion)                                    4.662e-02  1.048e+00  1.239e+00  0.038 0.969996    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.539e+01  2.069e-07  4.208e+03 -0.004 0.997082    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.095e+00  9.130e-01   0.75490    1.5890
Age                                                       1.039e+00  9.622e-01   1.01325    1.0660
Gendermale                                                1.774e+00  5.638e-01   1.12785    2.7890
Hypertension.compositeno                                  5.961e-01  1.678e+00   0.28453    1.2487
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.569e-01  1.045e+00   0.61778    1.4822
SmokerCurrentno                                           5.475e-01  1.826e+00   0.36664    0.8176
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.401e+00  7.140e-01   0.91454    2.1451
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.496e+00  6.686e-01   0.90098    2.4831
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.812e-01  1.019e+00   0.97167    0.9908
BMI                                                       1.059e+00  9.442e-01   1.00659    1.1145
CAD_history                                               1.152e+00  8.684e-01   0.77330    1.7150
Stroke_history                                            1.044e+00  9.579e-01   0.70161    1.5534
Peripheral.interv                                         1.881e+00  5.316e-01   1.22621    2.8853
stenose0-49%                                              1.659e-07  6.027e+06   0.00000       Inf
stenose50-70%                                             4.266e-01  2.344e+00   0.07765    2.3434
stenose70-90%                                             7.443e-01  1.343e+00   0.17924    3.0909
stenose90-99%                                             7.617e-01  1.313e+00   0.18446    3.1454
stenose100% (Occlusion)                                   1.048e+00  9.545e-01   0.09231   11.8918
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.069e-07  4.833e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.705  (se = 0.023 )
Likelihood ratio test= 68.36  on 19 df,   p=2e-07
Wald test            = 38.72  on 19 df,   p=0.005
Score (logrank) test = 66.86  on 19 df,   p=3e-07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: 0.090966 
Standard error............: 0.189871 
Odds ratio (effect size)..: 1.095 
Lower 95% CI..............: 0.755 
Upper 95% CI..............: 1.589 
T-value...................: 0.479096 
P-value...................: 0.6318701 
Sample size in model......: 1002 
Number of events..........: 114 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 1006, number of events= 119 
   (1382 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]  3.149e-01  1.370e+00  1.929e-01  1.633 0.102540    
Age                                                        3.419e-02  1.035e+00  1.261e-02  2.711 0.006713 ** 
Gendermale                                                 4.342e-01  1.544e+00  2.199e-01  1.974 0.048335 *  
Hypertension.compositeno                                  -4.432e-01  6.420e-01  3.576e-01 -1.239 0.215200    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -5.992e-02  9.418e-01  2.208e-01 -0.271 0.786088    
SmokerCurrentno                                           -5.022e-01  6.052e-01  2.014e-01 -2.493 0.012653 *  
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           2.893e-01  1.336e+00  2.159e-01  1.340 0.180144    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     3.779e-01  1.459e+00  2.578e-01  1.466 0.142612    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -1.788e-02  9.823e-01  4.918e-03 -3.635 0.000278 ***
BMI                                                        5.699e-02  1.059e+00  2.656e-02  2.146 0.031874 *  
CAD_history                                                1.903e-01  1.210e+00  1.989e-01  0.956 0.338836    
Stroke_history                                             3.055e-02  1.031e+00  1.974e-01  0.155 0.876999    
Peripheral.interv                                          5.619e-01  1.754e+00  2.131e-01  2.637 0.008368 ** 
stenose0-49%                                              -1.540e+01  2.045e-07  3.123e+03 -0.005 0.996065    
stenose50-70%                                             -8.794e-01  4.150e-01  8.686e-01 -1.012 0.311330    
stenose70-90%                                             -3.666e-01  6.931e-01  7.269e-01 -0.504 0.613981    
stenose90-99%                                             -3.659e-01  6.936e-01  7.256e-01 -0.504 0.614096    
stenose100% (Occlusion)                                   -5.671e-02  9.449e-01  1.238e+00 -0.046 0.963452    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.519e+01  2.528e-07  2.805e+03 -0.005 0.995679    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.370e+00  7.299e-01   0.93882    1.9996
Age                                                       1.035e+00  9.664e-01   1.00951    1.0607
Gendermale                                                1.544e+00  6.478e-01   1.00318    2.3753
Hypertension.compositeno                                  6.420e-01  1.558e+00   0.31849    1.2939
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.418e-01  1.062e+00   0.61101    1.4518
SmokerCurrentno                                           6.052e-01  1.652e+00   0.40784    0.8981
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.336e+00  7.488e-01   0.87479    2.0390
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.459e+00  6.853e-01   0.88047    2.4185
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.823e-01  1.018e+00   0.97286    0.9918
BMI                                                       1.059e+00  9.446e-01   1.00495    1.1152
CAD_history                                               1.210e+00  8.267e-01   0.81904    1.7863
Stroke_history                                            1.031e+00  9.699e-01   0.70028    1.5180
Peripheral.interv                                         1.754e+00  5.701e-01   1.15517    2.6634
stenose0-49%                                              2.045e-07  4.889e+06   0.00000       Inf
stenose50-70%                                             4.150e-01  2.409e+00   0.07564    2.2773
stenose70-90%                                             6.931e-01  1.443e+00   0.16675    2.8806
stenose90-99%                                             6.936e-01  1.442e+00   0.16730    2.8756
stenose100% (Occlusion)                                   9.449e-01  1.058e+00   0.08354   10.6868
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.528e-07  3.956e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.694  (se = 0.023 )
Likelihood ratio test= 64.3  on 19 df,   p=8e-07
Wald test            = 60.58  on 19 df,   p=3e-06
Score (logrank) test = 64.14  on 19 df,   p=8e-07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.314907 
Standard error............: 0.19288 
Odds ratio (effect size)..: 1.37 
Lower 95% CI..............: 0.939 
Upper 95% CI..............: 2 
T-value...................: 1.632661 
P-value...................: 0.1025404 
Sample size in model......: 1006 
Number of events..........: 119 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 1044, number of events= 120 
   (1344 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148]  1.748e-01  1.191e+00  1.860e-01  0.940  0.34727    
Age                                                        3.379e-02  1.034e+00  1.249e-02  2.704  0.00684 ** 
Gendermale                                                 4.474e-01  1.564e+00  2.179e-01  2.053  0.04006 *  
Hypertension.compositeno                                  -4.648e-01  6.283e-01  3.578e-01 -1.299  0.19387    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -4.790e-02  9.532e-01  2.188e-01 -0.219  0.82676    
SmokerCurrentno                                           -5.109e-01  6.000e-01  2.003e-01 -2.550  0.01076 *  
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           3.401e-01  1.405e+00  2.130e-01  1.597  0.11036    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     3.623e-01  1.437e+00  2.571e-01  1.409  0.15872    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -1.979e-02  9.804e-01  4.879e-03 -4.056 4.99e-05 ***
BMI                                                        5.687e-02  1.059e+00  2.555e-02  2.226  0.02603 *  
CAD_history                                                1.679e-01  1.183e+00  1.979e-01  0.848  0.39636    
Stroke_history                                             3.892e-02  1.040e+00  1.978e-01  0.197  0.84402    
Peripheral.interv                                          5.626e-01  1.755e+00  2.131e-01  2.641  0.00827 ** 
stenose0-49%                                              -1.548e+01  1.894e-07  2.739e+03 -0.006  0.99549    
stenose50-70%                                             -8.329e-01  4.348e-01  8.698e-01 -0.958  0.33825    
stenose70-90%                                             -2.432e-01  7.841e-01  7.270e-01 -0.334  0.73803    
stenose90-99%                                             -1.974e-01  8.209e-01  7.252e-01 -0.272  0.78546    
stenose100% (Occlusion)                                    1.205e-01  1.128e+00  1.242e+00  0.097  0.92272    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.518e+01  2.560e-07  2.870e+03 -0.005  0.99578    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.191e+00  8.396e-01   0.82721    1.7148
Age                                                       1.034e+00  9.668e-01   1.00934    1.0600
Gendermale                                                1.564e+00  6.393e-01   1.02051    2.3975
Hypertension.compositeno                                  6.283e-01  1.592e+00   0.31162    1.2667
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.532e-01  1.049e+00   0.62075    1.4638
SmokerCurrentno                                           6.000e-01  1.667e+00   0.40514    0.8884
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.405e+00  7.117e-01   0.92552    2.1331
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.437e+00  6.961e-01   0.86802    2.3778
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.804e-01  1.020e+00   0.97107    0.9898
BMI                                                       1.059e+00  9.447e-01   1.00681    1.1129
CAD_history                                               1.183e+00  8.455e-01   0.80247    1.7433
Stroke_history                                            1.040e+00  9.618e-01   0.70553    1.5321
Peripheral.interv                                         1.755e+00  5.697e-01   1.15608    2.6651
stenose0-49%                                              1.894e-07  5.281e+06   0.00000       Inf
stenose50-70%                                             4.348e-01  2.300e+00   0.07905    2.3912
stenose70-90%                                             7.841e-01  1.275e+00   0.18860    3.2602
stenose90-99%                                             8.209e-01  1.218e+00   0.19814    3.4007
stenose100% (Occlusion)                                   1.128e+00  8.865e-01   0.09893   12.8612
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.560e-07  3.906e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.696  (se = 0.023 )
Likelihood ratio test= 65.33  on 19 df,   p=5e-07
Wald test            = 60.85  on 19 df,   p=3e-06
Score (logrank) test = 63.96  on 19 df,   p=9e-07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.174792 
Standard error............: 0.185968 
Odds ratio (effect size)..: 1.191 
Lower 95% CI..............: 0.827 
Upper 95% CI..............: 1.715 
T-value...................: 0.939904 
P-value...................: 0.3472667 
Sample size in model......: 1044 
Number of events..........: 120 
* Analyzing the effect of plaque proteins on [epstroke.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 477, number of events= 32 
   (1911 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694]  2.472e-02  1.025e+00  3.648e-01  0.068    0.946
Age                                                        3.546e-02  1.036e+00  2.467e-02  1.438    0.151
Gendermale                                                 3.450e-02  1.035e+00  4.022e-01  0.086    0.932
Hypertension.compositeno                                  -7.437e-01  4.754e-01  7.516e-01 -0.990    0.322
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA
DiabetesStatusDiabetes                                     5.315e-01  1.701e+00  4.207e-01  1.263    0.206
SmokerCurrentno                                           -5.350e-01  5.857e-01  3.815e-01 -1.402    0.161
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA
Med.Statin.LLDno                                          -2.487e-02  9.754e-01  4.179e-01 -0.060    0.953
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA
Med.all.antiplateletno                                     3.103e-01  1.364e+00  5.848e-01  0.531    0.596
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA
GFR_MDRD                                                  -8.165e-04  9.992e-01  9.773e-03 -0.084    0.933
BMI                                                       -1.288e-02  9.872e-01  4.814e-02 -0.268    0.789
CAD_history                                                2.559e-01  1.292e+00  3.977e-01  0.643    0.520
Stroke_history                                             3.207e-01  1.378e+00  3.689e-01  0.869    0.385
Peripheral.interv                                         -5.758e-01  5.622e-01  5.473e-01 -1.052    0.293
stenose0-49%                                              -1.810e+01  1.377e-08  1.222e+04 -0.001    0.999
stenose50-70%                                             -1.785e+01  1.766e-08  4.805e+03 -0.004    0.997
stenose70-90%                                             -8.478e-01  4.283e-01  1.142e+00 -0.742    0.458
stenose90-99%                                             -9.721e-01  3.783e-01  1.153e+00 -0.843    0.399
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA
stenoseNA                                                         NA         NA  0.000e+00     NA       NA
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA
stenose99                                                         NA         NA  0.000e+00     NA       NA

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 1.025e+00  9.756e-01   0.50142     2.095
Age                                                       1.036e+00  9.652e-01   0.98720     1.087
Gendermale                                                1.035e+00  9.661e-01   0.47054     2.277
Hypertension.compositeno                                  4.754e-01  2.104e+00   0.10897     2.074
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.701e+00  5.877e-01   0.74601     3.881
SmokerCurrentno                                           5.857e-01  1.708e+00   0.27727     1.237
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          9.754e-01  1.025e+00   0.43002     2.213
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.364e+00  7.332e-01   0.43347     4.291
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.992e-01  1.001e+00   0.98023     1.019
BMI                                                       9.872e-01  1.013e+00   0.89831     1.085
CAD_history                                               1.292e+00  7.742e-01   0.59235     2.816
Stroke_history                                            1.378e+00  7.256e-01   0.66875     2.840
Peripheral.interv                                         5.622e-01  1.779e+00   0.19233     1.644
stenose0-49%                                              1.377e-08  7.260e+07   0.00000       Inf
stenose50-70%                                             1.766e-08  5.662e+07   0.00000       Inf
stenose70-90%                                             4.283e-01  2.335e+00   0.04564     4.020
stenose90-99%                                             3.783e-01  2.643e+00   0.03946     3.627
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.712  (se = 0.04 )
Likelihood ratio test= 13.18  on 17 df,   p=0.7
Wald test            = 9.66  on 17 df,   p=0.9
Score (logrank) test = 11.9  on 17 df,   p=0.8


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: IL6_rank 
Effect size...............: 0.024717 
Standard error............: 0.364815 
Odds ratio (effect size)..: 1.025 
Lower 95% CI..............: 0.501 
Upper 95% CI..............: 2.095 
T-value...................: 0.067752 
P-value...................: 0.9459834 
Sample size in model......: 477 
Number of events..........: 32 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 509, number of events= 33 
   (1879 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -4.721e-01  6.237e-01  3.625e-01 -1.302    0.193
Age                                                        2.320e-02  1.023e+00  2.358e-02  0.984    0.325
Gendermale                                                 6.449e-02  1.067e+00  3.990e-01  0.162    0.872
Hypertension.compositeno                                  -8.685e-01  4.196e-01  7.492e-01 -1.159    0.246
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA
DiabetesStatusDiabetes                                     3.761e-01  1.457e+00  4.161e-01  0.904    0.366
SmokerCurrentno                                           -4.557e-01  6.340e-01  3.760e-01 -1.212    0.226
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA
Med.Statin.LLDno                                           4.243e-02  1.043e+00  4.107e-01  0.103    0.918
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA
Med.all.antiplateletno                                     1.006e-01  1.106e+00  5.873e-01  0.171    0.864
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA
GFR_MDRD                                                   1.256e-03  1.001e+00  9.624e-03  0.130    0.896
BMI                                                        1.816e-02  1.018e+00  4.546e-02  0.399    0.690
CAD_history                                                7.676e-02  1.080e+00  3.981e-01  0.193    0.847
Stroke_history                                             4.080e-01  1.504e+00  3.637e-01  1.122    0.262
Peripheral.interv                                         -4.789e-01  6.195e-01  5.459e-01 -0.877    0.380
stenose0-49%                                              -1.860e+01  8.342e-09  1.262e+04 -0.001    0.999
stenose50-70%                                             -1.841e+01  1.006e-08  4.635e+03 -0.004    0.997
stenose70-90%                                             -1.288e+00  2.757e-01  1.118e+00 -1.153    0.249
stenose90-99%                                             -1.414e+00  2.433e-01  1.134e+00 -1.246    0.213
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA
stenoseNA                                                         NA         NA  0.000e+00     NA       NA
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA
stenose99                                                         NA         NA  0.000e+00     NA       NA

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 6.237e-01  1.603e+00   0.30651     1.269
Age                                                       1.023e+00  9.771e-01   0.97724     1.072
Gendermale                                                1.067e+00  9.375e-01   0.48793     2.332
Hypertension.compositeno                                  4.196e-01  2.383e+00   0.09662     1.822
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.457e+00  6.865e-01   0.64443     3.292
SmokerCurrentno                                           6.340e-01  1.577e+00   0.30344     1.325
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.043e+00  9.585e-01   0.46651     2.333
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.106e+00  9.043e-01   0.34980     3.496
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  1.001e+00  9.987e-01   0.98255     1.020
BMI                                                       1.018e+00  9.820e-01   0.93151     1.113
CAD_history                                               1.080e+00  9.261e-01   0.49485     2.356
Stroke_history                                            1.504e+00  6.650e-01   0.73718     3.068
Peripheral.interv                                         6.195e-01  1.614e+00   0.21250     1.806
stenose0-49%                                              8.342e-09  1.199e+08   0.00000       Inf
stenose50-70%                                             1.006e-08  9.940e+07   0.00000       Inf
stenose70-90%                                             2.757e-01  3.627e+00   0.03082     2.466
stenose90-99%                                             2.433e-01  4.110e+00   0.02633     2.248
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.687  (se = 0.042 )
Likelihood ratio test= 13.23  on 17 df,   p=0.7
Wald test            = 10.29  on 17 df,   p=0.9
Score (logrank) test = 12.25  on 17 df,   p=0.8


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.472091 
Standard error............: 0.36246 
Odds ratio (effect size)..: 0.624 
Lower 95% CI..............: 0.307 
Upper 95% CI..............: 1.269 
T-value...................: -1.302464 
P-value...................: 0.1927578 
Sample size in model......: 509 
Number of events..........: 33 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 1002, number of events= 58 
   (1386 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] -1.438e-01  8.660e-01  2.656e-01 -0.541  0.58818   
Age                                                        5.026e-02  1.052e+00  1.793e-02  2.803  0.00506 **
Gendermale                                                 2.919e-01  1.339e+00  3.000e-01  0.973  0.33043   
Hypertension.compositeno                                  -2.080e-01  8.122e-01  4.442e-01 -0.468  0.63956   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                    -6.073e-02  9.411e-01  3.197e-01 -0.190  0.84935   
SmokerCurrentno                                           -3.427e-01  7.098e-01  2.939e-01 -1.166  0.24358   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                           3.978e-01  1.488e+00  2.912e-01  1.366  0.17192   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     3.468e-01  1.414e+00  3.748e-01  0.925  0.35485   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -5.639e-03  9.944e-01  7.004e-03 -0.805  0.42078   
BMI                                                        9.236e-02  1.097e+00  3.357e-02  2.751  0.00593 **
CAD_history                                               -5.750e-01  5.627e-01  3.288e-01 -1.749  0.08031 . 
Stroke_history                                             3.241e-01  1.383e+00  2.748e-01  1.179  0.23826   
Peripheral.interv                                          4.887e-01  1.630e+00  3.276e-01  1.492  0.13578   
stenose0-49%                                              -1.559e+01  1.699e-07  3.740e+03 -0.004  0.99667   
stenose50-70%                                             -5.898e-01  5.544e-01  1.159e+00 -0.509  0.61097   
stenose70-90%                                             -3.521e-01  7.032e-01  1.025e+00 -0.343  0.73127   
stenose90-99%                                             -3.578e-01  6.992e-01  1.024e+00 -0.349  0.72672   
stenose100% (Occlusion)                                    3.875e-01  1.473e+00  1.439e+00  0.269  0.78767   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                             -1.537e+01  2.109e-07  5.615e+03 -0.003  0.99782   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 8.660e-01  1.155e+00   0.51454     1.458
Age                                                       1.052e+00  9.510e-01   1.01523     1.089
Gendermale                                                1.339e+00  7.468e-01   0.74380     2.411
Hypertension.compositeno                                  8.122e-01  1.231e+00   0.34009     1.940
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.411e-01  1.063e+00   0.50289     1.761
SmokerCurrentno                                           7.098e-01  1.409e+00   0.39898     1.263
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.488e+00  6.718e-01   0.84120     2.634
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.414e+00  7.070e-01   0.67854     2.949
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.944e-01  1.006e+00   0.98082     1.008
BMI                                                       1.097e+00  9.118e-01   1.02692     1.171
CAD_history                                               5.627e-01  1.777e+00   0.29542     1.072
Stroke_history                                            1.383e+00  7.232e-01   0.80691     2.370
Peripheral.interv                                         1.630e+00  6.134e-01   0.85779     3.098
stenose0-49%                                              1.699e-07  5.887e+06   0.00000       Inf
stenose50-70%                                             5.544e-01  1.804e+00   0.05713     5.380
stenose70-90%                                             7.032e-01  1.422e+00   0.09429     5.245
stenose90-99%                                             6.992e-01  1.430e+00   0.09401     5.200
stenose100% (Occlusion)                                   1.473e+00  6.788e-01   0.08784    24.711
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.109e-07  4.742e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.682  (se = 0.036 )
Likelihood ratio test= 27.19  on 19 df,   p=0.1
Wald test            = 22.76  on 19 df,   p=0.2
Score (logrank) test = 26.28  on 19 df,   p=0.1


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: -0.143835 
Standard error............: 0.265636 
Odds ratio (effect size)..: 0.866 
Lower 95% CI..............: 0.515 
Upper 95% CI..............: 1.458 
T-value...................: -0.541475 
P-value...................: 0.5881802 
Sample size in model......: 1002 
Number of events..........: 58 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 1006, number of events= 61 
   (1382 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]  9.097e-02  1.095e+00  2.649e-01  0.343  0.73127   
Age                                                        4.173e-02  1.043e+00  1.733e-02  2.408  0.01603 * 
Gendermale                                                 2.095e-01  1.233e+00  2.916e-01  0.719  0.47243   
Hypertension.compositeno                                  -1.100e-01  8.958e-01  4.146e-01 -0.265  0.79079   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                    -9.138e-02  9.127e-01  3.159e-01 -0.289  0.77242   
SmokerCurrentno                                           -1.889e-01  8.279e-01  2.901e-01 -0.651  0.51499   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                           4.222e-01  1.525e+00  2.868e-01  1.472  0.14104   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     3.050e-01  1.357e+00  3.718e-01  0.820  0.41197   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -3.051e-03  9.970e-01  7.024e-03 -0.434  0.66402   
BMI                                                        9.994e-02  1.105e+00  3.516e-02  2.842  0.00448 **
CAD_history                                               -4.477e-01  6.391e-01  3.134e-01 -1.429  0.15306   
Stroke_history                                             3.724e-01  1.451e+00  2.649e-01  1.406  0.15973   
Peripheral.interv                                          5.374e-01  1.712e+00  3.163e-01  1.699  0.08937 . 
stenose0-49%                                              -1.514e+01  2.649e-07  4.536e+03 -0.003  0.99734   
stenose50-70%                                             -6.162e-01  5.400e-01  1.159e+00 -0.532  0.59478   
stenose70-90%                                             -3.140e-01  7.305e-01  1.023e+00 -0.307  0.75884   
stenose90-99%                                             -4.118e-01  6.625e-01  1.025e+00 -0.402  0.68783   
stenose100% (Occlusion)                                    3.763e-01  1.457e+00  1.435e+00  0.262  0.79312   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                             -1.530e+01  2.262e-07  3.904e+03 -0.004  0.99687   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.095e+00  9.130e-01   0.65167     1.841
Age                                                       1.043e+00  9.591e-01   1.00780     1.079
Gendermale                                                1.233e+00  8.110e-01   0.69627     2.184
Hypertension.compositeno                                  8.958e-01  1.116e+00   0.39748     2.019
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.127e-01  1.096e+00   0.49134     1.695
SmokerCurrentno                                           8.279e-01  1.208e+00   0.46886     1.462
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.525e+00  6.556e-01   0.86938     2.676
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.357e+00  7.371e-01   0.65464     2.812
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.970e-01  1.003e+00   0.98332     1.011
BMI                                                       1.105e+00  9.049e-01   1.03152     1.184
CAD_history                                               6.391e-01  1.565e+00   0.34580     1.181
Stroke_history                                            1.451e+00  6.891e-01   0.86351     2.439
Peripheral.interv                                         1.712e+00  5.843e-01   0.92068     3.182
stenose0-49%                                              2.649e-07  3.775e+06   0.00000       Inf
stenose50-70%                                             5.400e-01  1.852e+00   0.05575     5.230
stenose70-90%                                             7.305e-01  1.369e+00   0.09839     5.424
stenose90-99%                                             6.625e-01  1.510e+00   0.08888     4.938
stenose100% (Occlusion)                                   1.457e+00  6.864e-01   0.08754    24.245
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.262e-07  4.421e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.669  (se = 0.037 )
Likelihood ratio test= 24.74  on 19 df,   p=0.2
Wald test            = 22.97  on 19 df,   p=0.2
Score (logrank) test = 24.39  on 19 df,   p=0.2


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.090975 
Standard error............: 0.264899 
Odds ratio (effect size)..: 1.095 
Lower 95% CI..............: 0.652 
Upper 95% CI..............: 1.841 
T-value...................: 0.343431 
P-value...................: 0.731274 
Sample size in model......: 1006 
Number of events..........: 61 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 1044, number of events= 62 
   (1344 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148]  1.163e-01  1.123e+00  2.615e-01  0.445   0.6566   
Age                                                        4.236e-02  1.043e+00  1.715e-02  2.470   0.0135 * 
Gendermale                                                 2.109e-01  1.235e+00  2.871e-01  0.734   0.4627   
Hypertension.compositeno                                  -1.076e-01  8.980e-01  4.149e-01 -0.259   0.7954   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                    -4.084e-02  9.600e-01  3.103e-01 -0.132   0.8953   
SmokerCurrentno                                           -1.804e-01  8.349e-01  2.888e-01 -0.625   0.5322   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                           4.214e-01  1.524e+00  2.823e-01  1.493   0.1355   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     2.868e-01  1.332e+00  3.711e-01  0.773   0.4396   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -5.616e-03  9.944e-01  6.832e-03 -0.822   0.4111   
BMI                                                        9.215e-02  1.097e+00  3.244e-02  2.841   0.0045 **
CAD_history                                               -4.624e-01  6.298e-01  3.106e-01 -1.489   0.1366   
Stroke_history                                             3.497e-01  1.419e+00  2.651e-01  1.319   0.1871   
Peripheral.interv                                          4.865e-01  1.627e+00  3.152e-01  1.544   0.1227   
stenose0-49%                                              -1.547e+01  1.920e-07  3.676e+03 -0.004   0.9966   
stenose50-70%                                             -5.939e-01  5.522e-01  1.159e+00 -0.512   0.6085   
stenose70-90%                                             -2.858e-01  7.514e-01  1.025e+00 -0.279   0.7803   
stenose90-99%                                             -3.216e-01  7.250e-01  1.026e+00 -0.313   0.7541   
stenose100% (Occlusion)                                    4.722e-01  1.604e+00  1.441e+00  0.328   0.7431   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                             -1.528e+01  2.307e-07  3.934e+03 -0.004   0.9969   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.123e+00  8.902e-01   0.67282     1.875
Age                                                       1.043e+00  9.585e-01   1.00878     1.079
Gendermale                                                1.235e+00  8.099e-01   0.70336     2.168
Hypertension.compositeno                                  8.980e-01  1.114e+00   0.39817     2.025
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.600e-01  1.042e+00   0.52254     1.764
SmokerCurrentno                                           8.349e-01  1.198e+00   0.47408     1.471
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.524e+00  6.562e-01   0.87646     2.650
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.332e+00  7.507e-01   0.64369     2.757
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.944e-01  1.006e+00   0.98117     1.008
BMI                                                       1.097e+00  9.120e-01   1.02898     1.169
CAD_history                                               6.298e-01  1.588e+00   0.34258     1.158
Stroke_history                                            1.419e+00  7.049e-01   0.84381     2.385
Peripheral.interv                                         1.627e+00  6.148e-01   0.87701     3.017
stenose0-49%                                              1.920e-07  5.208e+06   0.00000       Inf
stenose50-70%                                             5.522e-01  1.811e+00   0.05691     5.358
stenose70-90%                                             7.514e-01  1.331e+00   0.10089     5.597
stenose90-99%                                             7.250e-01  1.379e+00   0.09697     5.421
stenose100% (Occlusion)                                   1.604e+00  6.236e-01   0.09522    27.006
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.307e-07  4.335e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.672  (se = 0.036 )
Likelihood ratio test= 25.52  on 19 df,   p=0.1
Wald test            = 23.76  on 19 df,   p=0.2
Score (logrank) test = 25.03  on 19 df,   p=0.2


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.116261 
Standard error............: 0.261501 
Odds ratio (effect size)..: 1.123 
Lower 95% CI..............: 0.673 
Upper 95% CI..............: 1.875 
T-value...................: 0.444593 
P-value...................: 0.6566139 
Sample size in model......: 1044 
Number of events..........: 62 
* Analyzing the effect of plaque proteins on [epcoronary.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 477, number of events= 43 
   (1911 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694]  1.378e-01  1.148e+00  3.168e-01  0.435   0.6635  
Age                                                        4.680e-02  1.048e+00  2.288e-02  2.046   0.0408 *
Gendermale                                                 8.433e-01  2.324e+00  4.519e-01  1.866   0.0620 .
Hypertension.compositeno                                  -5.478e-01  5.782e-01  6.192e-01 -0.885   0.3763  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                     4.049e-01  1.499e+00  3.626e-01  1.117   0.2642  
SmokerCurrentno                                           -4.949e-01  6.097e-01  3.286e-01 -1.506   0.1321  
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA  
Med.Statin.LLDno                                           9.778e-02  1.103e+00  3.667e-01  0.267   0.7897  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     3.919e-01  1.480e+00  4.544e-01  0.862   0.3885  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -7.231e-03  9.928e-01  8.598e-03 -0.841   0.4004  
BMI                                                        1.272e-02  1.013e+00  4.331e-02  0.294   0.7691  
CAD_history                                                7.817e-01  2.185e+00  3.375e-01  2.316   0.0205 *
Stroke_history                                            -2.712e-01  7.624e-01  3.572e-01 -0.759   0.4477  
Peripheral.interv                                          3.803e-01  1.463e+00  3.575e-01  1.064   0.2874  
stenose0-49%                                              -8.465e-01  4.289e-01  8.354e+03  0.000   0.9999  
stenose50-70%                                              1.550e+01  5.402e+06  4.119e+03  0.004   0.9970  
stenose70-90%                                              1.589e+01  7.997e+06  4.119e+03  0.004   0.9969  
stenose90-99%                                              1.577e+01  7.055e+06  4.119e+03  0.004   0.9969  
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 1.148e+00  8.713e-01    0.6169     2.135
Age                                                       1.048e+00  9.543e-01    1.0020     1.096
Gendermale                                                2.324e+00  4.303e-01    0.9584     5.635
Hypertension.compositeno                                  5.782e-01  1.729e+00    0.1718     1.946
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.499e+00  6.671e-01    0.7365     3.051
SmokerCurrentno                                           6.097e-01  1.640e+00    0.3201     1.161
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.103e+00  9.068e-01    0.5375     2.262
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.480e+00  6.758e-01    0.6073     3.605
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.928e-01  1.007e+00    0.9762     1.010
BMI                                                       1.013e+00  9.874e-01    0.9304     1.103
CAD_history                                               2.185e+00  4.576e-01    1.1278     4.234
Stroke_history                                            7.624e-01  1.312e+00    0.3786     1.536
Peripheral.interv                                         1.463e+00  6.837e-01    0.7259     2.947
stenose0-49%                                              4.289e-01  2.332e+00    0.0000       Inf
stenose50-70%                                             5.402e+06  1.851e-07    0.0000       Inf
stenose70-90%                                             7.997e+06  1.251e-07    0.0000       Inf
stenose90-99%                                             7.055e+06  1.417e-07    0.0000       Inf
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.749  (se = 0.033 )
Likelihood ratio test= 31.57  on 17 df,   p=0.02
Wald test            = 24.09  on 17 df,   p=0.1
Score (logrank) test = 32.09  on 17 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: IL6_rank 
Effect size...............: 0.137818 
Standard error............: 0.316752 
Odds ratio (effect size)..: 1.148 
Lower 95% CI..............: 0.617 
Upper 95% CI..............: 2.135 
T-value...................: 0.435097 
P-value...................: 0.6634919 
Sample size in model......: 477 
Number of events..........: 43 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 509, number of events= 44 
   (1879 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635]  2.557e-01  1.291e+00  3.158e-01  0.810   0.4182  
Age                                                        3.929e-02  1.040e+00  2.294e-02  1.713   0.0868 .
Gendermale                                                 6.478e-01  1.911e+00  4.234e-01  1.530   0.1260  
Hypertension.compositeno                                  -2.496e-01  7.791e-01  5.535e-01 -0.451   0.6520  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                     5.175e-01  1.678e+00  3.489e-01  1.483   0.1381  
SmokerCurrentno                                           -5.444e-01  5.802e-01  3.229e-01 -1.686   0.0918 .
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA  
Med.Statin.LLDno                                           9.463e-02  1.099e+00  3.667e-01  0.258   0.7964  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     3.216e-01  1.379e+00  4.555e-01  0.706   0.4802  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -1.149e-02  9.886e-01  8.315e-03 -1.382   0.1669  
BMI                                                        2.137e-02  1.022e+00  4.270e-02  0.500   0.6168  
CAD_history                                                7.843e-01  2.191e+00  3.274e-01  2.395   0.0166 *
Stroke_history                                            -1.394e-01  8.699e-01  3.382e-01 -0.412   0.6803  
Peripheral.interv                                          5.109e-01  1.667e+00  3.537e-01  1.445   0.1486  
stenose0-49%                                              -8.090e-01  4.453e-01  9.205e+03  0.000   0.9999  
stenose50-70%                                              1.566e+01  6.305e+06  5.158e+03  0.003   0.9976  
stenose70-90%                                              1.598e+01  8.730e+06  5.158e+03  0.003   0.9975  
stenose90-99%                                              1.591e+01  8.127e+06  5.158e+03  0.003   0.9975  
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 1.291e+00  7.744e-01    0.6954     2.398
Age                                                       1.040e+00  9.615e-01    0.9943     1.088
Gendermale                                                1.911e+00  5.232e-01    0.8335     4.383
Hypertension.compositeno                                  7.791e-01  1.284e+00    0.2633     2.306
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.678e+00  5.960e-01    0.8467     3.325
SmokerCurrentno                                           5.802e-01  1.724e+00    0.3081     1.092
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.099e+00  9.097e-01    0.5357     2.256
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.379e+00  7.250e-01    0.5648     3.368
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.886e-01  1.012e+00    0.9726     1.005
BMI                                                       1.022e+00  9.789e-01    0.9396     1.111
CAD_history                                               2.191e+00  4.565e-01    1.1532     4.162
Stroke_history                                            8.699e-01  1.150e+00    0.4483     1.688
Peripheral.interv                                         1.667e+00  6.000e-01    0.8333     3.334
stenose0-49%                                              4.453e-01  2.246e+00    0.0000       Inf
stenose50-70%                                             6.305e+06  1.586e-07    0.0000       Inf
stenose70-90%                                             8.730e+06  1.145e-07    0.0000       Inf
stenose90-99%                                             8.127e+06  1.230e-07    0.0000       Inf
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.731  (se = 0.036 )
Likelihood ratio test= 31  on 17 df,   p=0.02
Wald test            = 23.03  on 17 df,   p=0.1
Score (logrank) test = 32.35  on 17 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_rank 
Effect size...............: 0.25569 
Standard error............: 0.315832 
Odds ratio (effect size)..: 1.291 
Lower 95% CI..............: 0.695 
Upper 95% CI..............: 2.398 
T-value...................: 0.809576 
P-value...................: 0.4181839 
Sample size in model......: 509 
Number of events..........: 44 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 1002, number of events= 79 
   (1386 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061]  5.813e-02  1.060e+00  2.304e-01  0.252  0.80083    
Age                                                       -3.043e-03  9.970e-01  1.510e-02 -0.201  0.84032    
Gendermale                                                 7.141e-01  2.042e+00  3.011e-01  2.372  0.01771 *  
Hypertension.compositeno                                  -7.882e-01  4.546e-01  5.252e-01 -1.501  0.13341    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -1.803e-01  8.351e-01  2.782e-01 -0.648  0.51711    
SmokerCurrentno                                           -5.750e-01  5.627e-01  2.428e-01 -2.369  0.01785 *  
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           2.493e-01  1.283e+00  2.741e-01  0.909  0.36312    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.737e-01  1.190e+00  3.354e-01  0.518  0.60448    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -2.040e-02  9.798e-01  5.967e-03 -3.419  0.00063 ***
BMI                                                        1.169e-02  1.012e+00  3.338e-02  0.350  0.72620    
CAD_history                                                9.403e-01  2.561e+00  2.410e-01  3.901 9.56e-05 ***
Stroke_history                                            -1.024e-01  9.027e-01  2.526e-01 -0.405  0.68527    
Peripheral.interv                                          4.301e-01  1.537e+00  2.613e-01  1.646  0.09979 .  
stenose0-49%                                              -1.581e+01  1.357e-07  3.608e+03 -0.004  0.99650    
stenose50-70%                                             -1.055e+00  3.483e-01  1.233e+00 -0.856  0.39220    
stenose70-90%                                             -1.716e-02  9.830e-01  1.022e+00 -0.017  0.98661    
stenose90-99%                                             -1.699e-01  8.437e-01  1.022e+00 -0.166  0.86794    
stenose100% (Occlusion)                                   -1.551e+01  1.841e-07  3.150e+03 -0.005  0.99607    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                              1.291e+00  3.637e+00  1.459e+00  0.885  0.37628    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.060e+00  9.435e-01   0.67470    1.6649
Age                                                       9.970e-01  1.003e+00   0.96789    1.0269
Gendermale                                                2.042e+00  4.896e-01   1.13195    3.6847
Hypertension.compositeno                                  4.546e-01  2.200e+00   0.16241    1.2727
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    8.351e-01  1.198e+00   0.48403    1.4407
SmokerCurrentno                                           5.627e-01  1.777e+00   0.34966    0.9055
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.283e+00  7.793e-01   0.74978    2.1959
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.190e+00  8.405e-01   0.61655    2.2958
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.798e-01  1.021e+00   0.96842    0.9913
BMI                                                       1.012e+00  9.884e-01   0.94768    1.0802
CAD_history                                               2.561e+00  3.905e-01   1.59668    4.1070
Stroke_history                                            9.027e-01  1.108e+00   0.55019    1.4810
Peripheral.interv                                         1.537e+00  6.504e-01   0.92119    2.5661
stenose0-49%                                              1.357e-07  7.370e+06   0.00000       Inf
stenose50-70%                                             3.483e-01  2.871e+00   0.03108    3.9018
stenose70-90%                                             9.830e-01  1.017e+00   0.13256    7.2891
stenose90-99%                                             8.437e-01  1.185e+00   0.11388    6.2514
stenose100% (Occlusion)                                   1.841e-07  5.432e+06   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             3.637e+00  2.750e-01   0.20825   63.5179
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.738  (se = 0.027 )
Likelihood ratio test= 61.46  on 19 df,   p=2e-06
Wald test            = 60.06  on 19 df,   p=4e-06
Score (logrank) test = 64.83  on 19 df,   p=7e-07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: 0.058129 
Standard error............: 0.230422 
Odds ratio (effect size)..: 1.06 
Lower 95% CI..............: 0.675 
Upper 95% CI..............: 1.665 
T-value...................: 0.252272 
P-value...................: 0.8008308 
Sample size in model......: 1002 
Number of events..........: 79 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 1006, number of events= 81 
   (1382 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]  3.112e-01  1.365e+00  2.338e-01  1.331 0.183175    
Age                                                       -9.543e-04  9.990e-01  1.485e-02 -0.064 0.948766    
Gendermale                                                 5.574e-01  1.746e+00  2.851e-01  1.955 0.050527 .  
Hypertension.compositeno                                  -8.059e-01  4.467e-01  5.248e-01 -1.536 0.124600    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -1.867e-01  8.297e-01  2.766e-01 -0.675 0.499578    
SmokerCurrentno                                           -5.490e-01  5.775e-01  2.406e-01 -2.282 0.022506 *  
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           1.850e-01  1.203e+00  2.751e-01  0.673 0.501211    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     2.059e-01  1.229e+00  3.353e-01  0.614 0.539303    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -2.060e-02  9.796e-01  5.884e-03 -3.501 0.000464 ***
BMI                                                        7.733e-03  1.008e+00  3.365e-02  0.230 0.818252    
CAD_history                                                9.287e-01  2.531e+00  2.382e-01  3.898 9.69e-05 ***
Stroke_history                                            -1.891e-01  8.277e-01  2.508e-01 -0.754 0.450860    
Peripheral.interv                                          3.230e-01  1.381e+00  2.580e-01  1.252 0.210705    
stenose0-49%                                              -1.552e+01  1.820e-07  3.417e+03 -0.005 0.996376    
stenose50-70%                                             -1.047e+00  3.509e-01  1.231e+00 -0.851 0.394713    
stenose70-90%                                             -1.764e-01  8.383e-01  1.023e+00 -0.172 0.863099    
stenose90-99%                                             -2.621e-01  7.694e-01  1.023e+00 -0.256 0.797760    
stenose100% (Occlusion)                                   -1.525e+01  2.383e-07  2.534e+03 -0.006 0.995199    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                              9.092e-01  2.482e+00  1.444e+00  0.629 0.529066    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.365e+00  7.325e-01   0.86324    2.1588
Age                                                       9.990e-01  1.001e+00   0.97039    1.0286
Gendermale                                                1.746e+00  5.727e-01   0.99872    3.0529
Hypertension.compositeno                                  4.467e-01  2.239e+00   0.15970    1.2493
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    8.297e-01  1.205e+00   0.48248    1.4267
SmokerCurrentno                                           5.775e-01  1.732e+00   0.36039    0.9255
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.203e+00  8.311e-01   0.70178    2.0630
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.229e+00  8.140e-01   0.63674    2.3705
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.796e-01  1.021e+00   0.96838    0.9910
BMI                                                       1.008e+00  9.923e-01   0.94344    1.0765
CAD_history                                               2.531e+00  3.951e-01   1.58689    4.0375
Stroke_history                                            8.277e-01  1.208e+00   0.50632    1.3531
Peripheral.interv                                         1.381e+00  7.240e-01   0.83296    2.2903
stenose0-49%                                              1.820e-07  5.495e+06   0.00000       Inf
stenose50-70%                                             3.509e-01  2.850e+00   0.03145    3.9140
stenose70-90%                                             8.383e-01  1.193e+00   0.11291    6.2240
stenose90-99%                                             7.694e-01  1.300e+00   0.10364    5.7124
stenose100% (Occlusion)                                   2.383e-07  4.196e+06   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.482e+00  4.028e-01   0.14633   42.1104
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.73  (se = 0.029 )
Likelihood ratio test= 60.73  on 19 df,   p=3e-06
Wald test            = 60.13  on 19 df,   p=4e-06
Score (logrank) test = 63.68  on 19 df,   p=1e-06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.311249 
Standard error............: 0.233838 
Odds ratio (effect size)..: 1.365 
Lower 95% CI..............: 0.863 
Upper 95% CI..............: 2.159 
T-value...................: 1.331043 
P-value...................: 0.1831749 
Sample size in model......: 1006 
Number of events..........: 81 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 1044, number of events= 81 
   (1344 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] -1.596e-02  9.842e-01  2.268e-01 -0.070 0.943890    
Age                                                       -1.539e-03  9.985e-01  1.479e-02 -0.104 0.917138    
Gendermale                                                 6.124e-01  1.845e+00  2.852e-01  2.147 0.031760 *  
Hypertension.compositeno                                  -8.179e-01  4.414e-01  5.250e-01 -1.558 0.119285    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -2.171e-01  8.048e-01  2.764e-01 -0.785 0.432187    
SmokerCurrentno                                           -5.707e-01  5.651e-01  2.391e-01 -2.387 0.016980 *  
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           2.564e-01  1.292e+00  2.724e-01  0.941 0.346513    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.932e-01  1.213e+00  3.346e-01  0.577 0.563740    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -2.177e-02  9.785e-01  5.897e-03 -3.691 0.000223 ***
BMI                                                        9.025e-03  1.009e+00  3.301e-02  0.273 0.784560    
CAD_history                                                9.195e-01  2.508e+00  2.377e-01  3.868 0.000110 ***
Stroke_history                                            -1.476e-01  8.628e-01  2.513e-01 -0.587 0.556941    
Peripheral.interv                                          3.358e-01  1.399e+00  2.589e-01  1.297 0.194621    
stenose0-49%                                              -1.549e+01  1.880e-07  3.028e+03 -0.005 0.995919    
stenose50-70%                                             -1.074e+00  3.417e-01  1.233e+00 -0.871 0.383723    
stenose70-90%                                             -1.110e-01  8.949e-01  1.023e+00 -0.109 0.913589    
stenose90-99%                                             -1.822e-01  8.334e-01  1.023e+00 -0.178 0.858652    
stenose100% (Occlusion)                                   -1.520e+01  2.509e-07  2.633e+03 -0.006 0.995394    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                              1.014e+00  2.758e+00  1.440e+00  0.705 0.481061    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 9.842e-01  1.016e+00    0.6310    1.5351
Age                                                       9.985e-01  1.002e+00    0.9699    1.0278
Gendermale                                                1.845e+00  5.420e-01    1.0549    3.2265
Hypertension.compositeno                                  4.414e-01  2.266e+00    0.1577    1.2351
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    8.048e-01  1.242e+00    0.4682    1.3835
SmokerCurrentno                                           5.651e-01  1.770e+00    0.3537    0.9029
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.292e+00  7.738e-01    0.7577    2.2040
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.213e+00  8.244e-01    0.6296    2.3371
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.785e-01  1.022e+00    0.9672    0.9898
BMI                                                       1.009e+00  9.910e-01    0.9458    1.0765
CAD_history                                               2.508e+00  3.987e-01    1.5740    3.9966
Stroke_history                                            8.628e-01  1.159e+00    0.5272    1.4119
Peripheral.interv                                         1.399e+00  7.147e-01    0.8423    2.3240
stenose0-49%                                              1.880e-07  5.318e+06    0.0000       Inf
stenose50-70%                                             3.417e-01  2.926e+00    0.0305    3.8279
stenose70-90%                                             8.949e-01  1.117e+00    0.1204    6.6502
stenose90-99%                                             8.334e-01  1.200e+00    0.1122    6.1898
stenose100% (Occlusion)                                   2.509e-07  3.986e+06    0.0000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.758e+00  3.626e-01    0.1641   46.3478
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.732  (se = 0.027 )
Likelihood ratio test= 60.47  on 19 df,   p=3e-06
Wald test            = 59.13  on 19 df,   p=5e-06
Score (logrank) test = 62.13  on 19 df,   p=2e-06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: -0.015964 
Standard error............: 0.226816 
Odds ratio (effect size)..: 0.984 
Lower 95% CI..............: 0.631 
Upper 95% CI..............: 1.535 
T-value...................: -0.070382 
P-value...................: 0.9438898 
Sample size in model......: 1044 
Number of events..........: 81 
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 477, number of events= 23 
   (1911 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -9.187e-02  9.122e-01  4.309e-01 -0.213   0.8312  
Age                                                        6.195e-02  1.064e+00  3.229e-02  1.919   0.0550 .
Gendermale                                                 6.438e-01  1.904e+00  5.718e-01  1.126   0.2602  
Hypertension.compositeno                                  -1.812e+01  1.351e-08  4.520e+03 -0.004   0.9968  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                     6.838e-01  1.981e+00  5.048e-01  1.354   0.1756  
SmokerCurrentno                                           -7.494e-01  4.726e-01  4.470e-01 -1.677   0.0936 .
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA  
Med.Statin.LLDno                                           6.669e-01  1.948e+00  4.697e-01  1.420   0.1556  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     5.143e-01  1.672e+00  6.467e-01  0.795   0.4265  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -1.130e-02  9.888e-01  1.115e-02 -1.014   0.3107  
BMI                                                        3.610e-02  1.037e+00  5.620e-02  0.642   0.5207  
CAD_history                                                5.531e-01  1.739e+00  4.562e-01  1.212   0.2254  
Stroke_history                                             1.645e-01  1.179e+00  4.525e-01  0.364   0.7162  
Peripheral.interv                                          2.184e-01  1.244e+00  5.444e-01  0.401   0.6882  
stenose0-49%                                              -5.590e-01  5.718e-01  3.107e+04  0.000   1.0000  
stenose50-70%                                              4.197e-01  1.521e+00  1.643e+04  0.000   1.0000  
stenose70-90%                                              1.810e+01  7.291e+07  1.408e+04  0.001   0.9990  
stenose90-99%                                              1.800e+01  6.555e+07  1.408e+04  0.001   0.9990  
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 9.122e-01  1.096e+00    0.3920     2.123
Age                                                       1.064e+00  9.399e-01    0.9987     1.133
Gendermale                                                1.904e+00  5.253e-01    0.6207     5.839
Hypertension.compositeno                                  1.351e-08  7.403e+07    0.0000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.981e+00  5.047e-01    0.7366     5.329
SmokerCurrentno                                           4.726e-01  2.116e+00    0.1968     1.135
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.948e+00  5.133e-01    0.7760     4.892
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.672e+00  5.979e-01    0.4708     5.940
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.888e-01  1.011e+00    0.9674     1.011
BMI                                                       1.037e+00  9.645e-01    0.9286     1.157
CAD_history                                               1.739e+00  5.752e-01    0.7110     4.251
Stroke_history                                            1.179e+00  8.483e-01    0.4856     2.861
Peripheral.interv                                         1.244e+00  8.038e-01    0.4280     3.616
stenose0-49%                                              5.718e-01  1.749e+00    0.0000       Inf
stenose50-70%                                             1.521e+00  6.573e-01    0.0000       Inf
stenose70-90%                                             7.291e+07  1.372e-08    0.0000       Inf
stenose90-99%                                             6.555e+07  1.526e-08    0.0000       Inf
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.791  (se = 0.041 )
Likelihood ratio test= 26.72  on 17 df,   p=0.06
Wald test            = 9.85  on 17 df,   p=0.9
Score (logrank) test = 22.16  on 17 df,   p=0.2


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: IL6_rank 
Effect size...............: -0.091872 
Standard error............: 0.430914 
Odds ratio (effect size)..: 0.912 
Lower 95% CI..............: 0.392 
Upper 95% CI..............: 2.123 
T-value...................: -0.213202 
P-value...................: 0.8311691 
Sample size in model......: 477 
Number of events..........: 23 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 509, number of events= 24 
   (1879 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -1.771e-01  8.377e-01  4.270e-01 -0.415   0.6783  
Age                                                        5.662e-02  1.058e+00  3.156e-02  1.794   0.0728 .
Gendermale                                                 6.642e-01  1.943e+00  5.609e-01  1.184   0.2364  
Hypertension.compositeno                                  -1.815e+01  1.310e-08  4.307e+03 -0.004   0.9966  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                     6.356e-01  1.888e+00  5.042e-01  1.261   0.2075  
SmokerCurrentno                                           -8.101e-01  4.448e-01  4.374e-01 -1.852   0.0640 .
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA  
Med.Statin.LLDno                                           6.893e-01  1.992e+00  4.646e-01  1.484   0.1379  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     2.638e-01  1.302e+00  6.552e-01  0.403   0.6872  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -2.066e-02  9.796e-01  1.089e-02 -1.897   0.0578 .
BMI                                                        1.251e-02  1.013e+00  5.815e-02  0.215   0.8296  
CAD_history                                                2.554e-01  1.291e+00  4.455e-01  0.573   0.5665  
Stroke_history                                             2.185e-01  1.244e+00  4.350e-01  0.502   0.6154  
Peripheral.interv                                          5.211e-01  1.684e+00  5.060e-01  1.030   0.3031  
stenose0-49%                                              -7.104e-01  4.914e-01  3.570e+04  0.000   1.0000  
stenose50-70%                                              3.764e-01  1.457e+00  2.096e+04  0.000   1.0000  
stenose70-90%                                              1.826e+01  8.550e+07  1.922e+04  0.001   0.9992  
stenose90-99%                                              1.805e+01  6.936e+07  1.922e+04  0.001   0.9993  
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 8.377e-01  1.194e+00    0.3628     1.934
Age                                                       1.058e+00  9.450e-01    0.9948     1.126
Gendermale                                                1.943e+00  5.147e-01    0.6472     5.833
Hypertension.compositeno                                  1.310e-08  7.634e+07    0.0000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.888e+00  5.296e-01    0.7028     5.072
SmokerCurrentno                                           4.448e-01  2.248e+00    0.1887     1.048
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.992e+00  5.019e-01    0.8015     4.953
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.302e+00  7.681e-01    0.3605     4.702
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.796e-01  1.021e+00    0.9589     1.001
BMI                                                       1.013e+00  9.876e-01    0.9035     1.135
CAD_history                                               1.291e+00  7.746e-01    0.5391     3.091
Stroke_history                                            1.244e+00  8.037e-01    0.5305     2.918
Peripheral.interv                                         1.684e+00  5.939e-01    0.6246     4.540
stenose0-49%                                              4.914e-01  2.035e+00    0.0000       Inf
stenose50-70%                                             1.457e+00  6.863e-01    0.0000       Inf
stenose70-90%                                             8.550e+07  1.170e-08    0.0000       Inf
stenose90-99%                                             6.936e+07  1.442e-08    0.0000       Inf
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.801  (se = 0.04 )
Likelihood ratio test= 29.16  on 17 df,   p=0.03
Wald test            = 9.19  on 17 df,   p=0.9
Score (logrank) test = 24.7  on 17 df,   p=0.1


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.177103 
Standard error............: 0.426996 
Odds ratio (effect size)..: 0.838 
Lower 95% CI..............: 0.363 
Upper 95% CI..............: 1.934 
T-value...................: -0.414764 
P-value...................: 0.6783146 
Sample size in model......: 509 
Number of events..........: 24 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 1002, number of events= 35 
   (1386 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061]  3.124e-01  1.367e+00  3.495e-01  0.894 0.371352    
Age                                                        6.928e-02  1.072e+00  2.589e-02  2.676 0.007450 ** 
Gendermale                                                 1.013e+00  2.755e+00  4.933e-01  2.054 0.039942 *  
Hypertension.compositeno                                  -1.767e+01  2.123e-08  3.770e+03 -0.005 0.996261    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -5.545e-02  9.461e-01  4.082e-01 -0.136 0.891958    
SmokerCurrentno                                           -4.117e-01  6.625e-01  3.828e-01 -1.076 0.282106    
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           3.902e-02  1.040e+00  4.239e-01  0.092 0.926647    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.096e+00  2.991e+00  3.963e-01  2.765 0.005696 ** 
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -3.121e-02  9.693e-01  9.102e-03 -3.428 0.000607 ***
BMI                                                        6.702e-02  1.069e+00  5.147e-02  1.302 0.192850    
CAD_history                                                2.453e-01  1.278e+00  3.548e-01  0.692 0.489238    
Stroke_history                                            -1.405e-01  8.689e-01  3.867e-01 -0.363 0.716259    
Peripheral.interv                                          6.223e-01  1.863e+00  4.073e-01  1.528 0.126503    
stenose0-49%                                              -1.957e+01  3.162e-09  2.801e+04 -0.001 0.999442    
stenose50-70%                                             -8.921e-01  4.098e-01  1.238e+00 -0.720 0.471253    
stenose70-90%                                             -1.380e+00  2.515e-01  1.065e+00 -1.296 0.195019    
stenose90-99%                                             -8.273e-01  4.372e-01  1.042e+00 -0.794 0.427146    
stenose100% (Occlusion)                                   -1.912e+01  4.954e-09  1.969e+04 -0.001 0.999225    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.913e+01  4.932e-09  4.806e+04  0.000 0.999682    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.367e+00  7.317e-01   0.68899    2.7110
Age                                                       1.072e+00  9.331e-01   1.01871    1.1275
Gendermale                                                2.755e+00  3.630e-01   1.04766    7.2440
Hypertension.compositeno                                  2.123e-08  4.709e+07   0.00000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.461e-01  1.057e+00   0.42503    2.1058
SmokerCurrentno                                           6.625e-01  1.509e+00   0.31288    1.4029
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.040e+00  9.617e-01   0.45305    2.3865
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    2.991e+00  3.343e-01   1.37565    6.5034
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.693e-01  1.032e+00   0.95214    0.9867
BMI                                                       1.069e+00  9.352e-01   0.96671    1.1828
CAD_history                                               1.278e+00  7.824e-01   0.63761    2.5618
Stroke_history                                            8.689e-01  1.151e+00   0.40722    1.8539
Peripheral.interv                                         1.863e+00  5.367e-01   0.83869    4.1396
stenose0-49%                                              3.162e-09  3.162e+08   0.00000       Inf
stenose50-70%                                             4.098e-01  2.440e+00   0.03619    4.6407
stenose70-90%                                             2.515e-01  3.976e+00   0.03118    2.0286
stenose90-99%                                             4.372e-01  2.287e+00   0.05675    3.3690
stenose100% (Occlusion)                                   4.954e-09  2.018e+08   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             4.932e-09  2.028e+08   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.841  (se = 0.028 )
Likelihood ratio test= 59.89  on 19 df,   p=4e-06
Wald test            = 21.5  on 19 df,   p=0.3
Score (logrank) test = 56.6  on 19 df,   p=1e-05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: 0.312398 
Standard error............: 0.349461 
Odds ratio (effect size)..: 1.367 
Lower 95% CI..............: 0.689 
Upper 95% CI..............: 2.711 
T-value...................: 0.893944 
P-value...................: 0.3713521 
Sample size in model......: 1002 
Number of events..........: 35 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 1006, number of events= 35 
   (1382 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]  5.464e-01  1.727e+00  3.680e-01  1.485 0.137593    
Age                                                        6.965e-02  1.072e+00  2.601e-02  2.677 0.007422 ** 
Gendermale                                                 1.005e+00  2.732e+00  4.908e-01  2.048 0.040545 *  
Hypertension.compositeno                                  -1.772e+01  2.009e-08  3.857e+03 -0.005 0.996334    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                     4.218e-02  1.043e+00  4.083e-01  0.103 0.917718    
SmokerCurrentno                                           -3.826e-01  6.821e-01  3.836e-01 -0.997 0.318610    
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                          -3.352e-02  9.670e-01  4.260e-01 -0.079 0.937271    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.105e+00  3.019e+00  3.963e-01  2.788 0.005308 ** 
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -3.056e-02  9.699e-01  9.111e-03 -3.354 0.000795 ***
BMI                                                        6.759e-02  1.070e+00  5.231e-02  1.292 0.196361    
CAD_history                                                2.611e-01  1.298e+00  3.523e-01  0.741 0.458589    
Stroke_history                                            -1.680e-01  8.454e-01  3.863e-01 -0.435 0.663620    
Peripheral.interv                                          5.397e-01  1.716e+00  4.005e-01  1.348 0.177778    
stenose0-49%                                              -1.933e+01  4.012e-09  3.424e+04 -0.001 0.999549    
stenose50-70%                                             -9.530e-01  3.856e-01  1.235e+00 -0.772 0.440403    
stenose70-90%                                             -1.527e+00  2.172e-01  1.063e+00 -1.437 0.150749    
stenose90-99%                                             -1.042e+00  3.528e-01  1.045e+00 -0.997 0.318684    
stenose100% (Occlusion)                                   -1.934e+01  3.981e-09  2.180e+04 -0.001 0.999292    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.899e+01  5.679e-09  3.413e+04 -0.001 0.999556    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.727e+00  5.790e-01   0.83957    3.5528
Age                                                       1.072e+00  9.327e-01   1.01884    1.1282
Gendermale                                                2.732e+00  3.660e-01   1.04423    7.1500
Hypertension.compositeno                                  2.009e-08  4.978e+07   0.00000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.043e+00  9.587e-01   0.46857    2.3220
SmokerCurrentno                                           6.821e-01  1.466e+00   0.32163    1.4467
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          9.670e-01  1.034e+00   0.41961    2.2286
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    3.019e+00  3.313e-01   1.38825    6.5635
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.699e-01  1.031e+00   0.95273    0.9874
BMI                                                       1.070e+00  9.346e-01   0.96566    1.1854
CAD_history                                               1.298e+00  7.702e-01   0.65089    2.5901
Stroke_history                                            8.454e-01  1.183e+00   0.39652    1.8023
Peripheral.interv                                         1.716e+00  5.829e-01   0.78252    3.7609
stenose0-49%                                              4.012e-09  2.493e+08   0.00000       Inf
stenose50-70%                                             3.856e-01  2.593e+00   0.03426    4.3404
stenose70-90%                                             2.172e-01  4.604e+00   0.02706    1.7434
stenose90-99%                                             3.528e-01  2.835e+00   0.04551    2.7347
stenose100% (Occlusion)                                   3.981e-09  2.512e+08   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             5.679e-09  1.761e+08   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.839  (se = 0.03 )
Likelihood ratio test= 61.36  on 19 df,   p=2e-06
Wald test            = 24.03  on 19 df,   p=0.2
Score (logrank) test = 59.66  on 19 df,   p=4e-06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.546431 
Standard error............: 0.368013 
Odds ratio (effect size)..: 1.727 
Lower 95% CI..............: 0.84 
Upper 95% CI..............: 3.553 
T-value...................: 1.484815 
P-value...................: 0.1375929 
Sample size in model......: 1006 
Number of events..........: 35 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose, data = TEMP.DF)

  n= 1044, number of events= 35 
   (1344 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] -2.174e-02  9.785e-01  3.452e-01 -0.063 0.949793    
Age                                                        6.789e-02  1.070e+00  2.577e-02  2.635 0.008421 ** 
Gendermale                                                 1.041e+00  2.832e+00  4.920e-01  2.116 0.034334 *  
Hypertension.compositeno                                  -1.769e+01  2.075e-08  3.765e+03 -0.005 0.996251    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -2.627e-02  9.741e-01  4.067e-01 -0.065 0.948492    
SmokerCurrentno                                           -4.254e-01  6.535e-01  3.816e-01 -1.115 0.264940    
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           8.787e-02  1.092e+00  4.219e-01  0.208 0.834997    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.088e+00  2.970e+00  3.978e-01  2.736 0.006214 ** 
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -3.243e-02  9.681e-01  9.119e-03 -3.556 0.000376 ***
BMI                                                        6.918e-02  1.072e+00  5.133e-02  1.348 0.177742    
CAD_history                                                2.495e-01  1.283e+00  3.531e-01  0.707 0.479796    
Stroke_history                                            -1.160e-01  8.905e-01  3.884e-01 -0.299 0.765236    
Peripheral.interv                                          5.734e-01  1.774e+00  4.037e-01  1.421 0.155458    
stenose0-49%                                              -1.972e+01  2.716e-09  2.937e+04 -0.001 0.999464    
stenose50-70%                                             -9.727e-01  3.781e-01  1.239e+00 -0.785 0.432437    
stenose70-90%                                             -1.445e+00  2.358e-01  1.068e+00 -1.353 0.176192    
stenose90-99%                                             -9.011e-01  4.061e-01  1.048e+00 -0.860 0.389738    
stenose100% (Occlusion)                                   -1.926e+01  4.335e-09  2.009e+04 -0.001 0.999235    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.904e+01  5.407e-09  3.419e+04 -0.001 0.999556    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 9.785e-01  1.022e+00   0.49743    1.9248
Age                                                       1.070e+00  9.344e-01   1.01754    1.1257
Gendermale                                                2.832e+00  3.531e-01   1.07986    7.4287
Hypertension.compositeno                                  2.075e-08  4.820e+07   0.00000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.741e-01  1.027e+00   0.43893    2.1616
SmokerCurrentno                                           6.535e-01  1.530e+00   0.30931    1.3806
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.092e+00  9.159e-01   0.47761    2.4961
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    2.970e+00  3.368e-01   1.36177    6.4754
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.681e-01  1.033e+00   0.95094    0.9855
BMI                                                       1.072e+00  9.332e-01   0.96906    1.1851
CAD_history                                               1.283e+00  7.792e-01   0.64242    2.5638
Stroke_history                                            8.905e-01  1.123e+00   0.41589    1.9066
Peripheral.interv                                         1.774e+00  5.636e-01   0.80432    3.9142
stenose0-49%                                              2.716e-09  3.681e+08   0.00000       Inf
stenose50-70%                                             3.781e-01  2.645e+00   0.03333    4.2880
stenose70-90%                                             2.358e-01  4.241e+00   0.02906    1.9132
stenose90-99%                                             4.061e-01  2.462e+00   0.05211    3.1654
stenose100% (Occlusion)                                   4.335e-09  2.307e+08   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             5.407e-09  1.849e+08   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA

Concordance= 0.839  (se = 0.029 )
Likelihood ratio test= 60.04  on 19 df,   p=4e-06
Wald test            = 21.96  on 19 df,   p=0.3
Score (logrank) test = 56.98  on 19 df,   p=1e-05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: -0.021735 
Standard error............: 0.345188 
Odds ratio (effect size)..: 0.978 
Lower 95% CI..............: 0.497 
Upper 95% CI..............: 1.925 
T-value...................: -0.062967 
P-value...................: 0.9497931 
Sample size in model......: 1044 
Number of events..........: 35 

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)
object 'head.style' not found

MODEL 3

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 3 same to model 2, with additional adjustments for circulating CRP levels
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +  Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))

    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL3.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}
* Analyzing the effect of plaque proteins on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 268, number of events= 35 
   (2120 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -5.459e-01  5.793e-01  3.897e-01 -1.401  0.16128    
Age                                                        1.227e-01  1.131e+00  3.092e-02  3.969 7.23e-05 ***
Gendermale                                                 1.010e+00  2.746e+00  5.246e-01  1.926  0.05415 .  
Hypertension.compositeno                                  -4.907e-01  6.122e-01  6.472e-01 -0.758  0.44835    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                     1.211e+00  3.355e+00  4.305e-01  2.812  0.00492 ** 
SmokerCurrentno                                           -5.491e-01  5.775e-01  3.795e-01 -1.447  0.14788    
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           7.838e-02  1.082e+00  4.133e-01  0.190  0.84961    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                    -7.814e-01  4.578e-01  8.461e-01 -0.924  0.35571    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                   7.143e-03  1.007e+00  1.087e-02  0.657  0.51118    
BMI                                                        1.431e-03  1.001e+00  5.364e-02  0.027  0.97871    
CAD_history                                                5.748e-01  1.777e+00  3.866e-01  1.487  0.13705    
Stroke_history                                            -1.171e-01  8.895e-01  4.013e-01 -0.292  0.77037    
Peripheral.interv                                          1.034e-01  1.109e+00  4.582e-01  0.226  0.82151    
stenose0-49%                                              -1.938e+01  3.824e-09  1.012e+04 -0.002  0.99847    
stenose50-70%                                             -1.844e+01  9.795e-09  3.557e+03 -0.005  0.99586    
stenose70-90%                                             -2.610e+00  7.353e-02  1.186e+00 -2.201  0.02774 *  
stenose90-99%                                             -3.683e+00  2.514e-02  1.248e+00 -2.950  0.00317 ** 
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
hsCRP_plasma                                               5.765e-03  1.006e+00  1.816e-03  3.175  0.00150 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 5.793e-01  1.726e+00  0.269928    1.2435
Age                                                       1.131e+00  8.845e-01  1.064087    1.2012
Gendermale                                                2.746e+00  3.642e-01  0.982133    7.6770
Hypertension.compositeno                                  6.122e-01  1.633e+00  0.172207    2.1766
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    3.355e+00  2.980e-01  1.443127    7.8007
SmokerCurrentno                                           5.775e-01  1.732e+00  0.274482    1.2149
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.082e+00  9.246e-01  0.481055    2.4315
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    4.578e-01  2.185e+00  0.087191    2.4033
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  1.007e+00  9.929e-01  0.985934    1.0289
BMI                                                       1.001e+00  9.986e-01  0.901503    1.1124
CAD_history                                               1.777e+00  5.628e-01  0.832850    3.7908
Stroke_history                                            8.895e-01  1.124e+00  0.405065    1.9531
Peripheral.interv                                         1.109e+00  9.018e-01  0.451705    2.7223
stenose0-49%                                              3.824e-09  2.615e+08  0.000000       Inf
stenose50-70%                                             9.795e-09  1.021e+08  0.000000       Inf
stenose70-90%                                             7.353e-02  1.360e+01  0.007196    0.7514
stenose90-99%                                             2.514e-02  3.978e+01  0.002176    0.2904
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.006e+00  9.943e-01  1.002209    1.0094

Concordance= 0.811  (se = 0.035 )
Likelihood ratio test= 57.83  on 18 df,   p=5e-06
Wald test            = 36.37  on 18 df,   p=0.006
Score (logrank) test = 99.38  on 18 df,   p=3e-13


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: IL6_rank 
Effect size...............: -0.545852 
Standard error............: 0.389675 
Odds ratio (effect size)..: 0.579 
Lower 95% CI..............: 0.27 
Upper 95% CI..............: 1.243 
T-value...................: -1.400788 
P-value...................: 0.1612775 
Sample size in model......: 268 
Number of events..........: 35 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 282, number of events= 37 
   (2106 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -8.868e-01  4.120e-01  3.678e-01 -2.411 0.015921 *  
Age                                                        1.015e-01  1.107e+00  2.855e-02  3.555 0.000377 ***
Gendermale                                                 1.123e+00  3.073e+00  5.091e-01  2.205 0.027422 *  
Hypertension.compositeno                                  -4.126e-01  6.619e-01  6.362e-01 -0.649 0.516564    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                     7.775e-01  2.176e+00  4.208e-01  1.848 0.064627 .  
SmokerCurrentno                                           -6.027e-01  5.474e-01  3.683e-01 -1.636 0.101762    
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           3.050e-02  1.031e+00  4.058e-01  0.075 0.940097    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                    -1.373e+00  2.532e-01  8.302e-01 -1.654 0.098072 .  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                   6.310e-04  1.001e+00  1.018e-02  0.062 0.950582    
BMI                                                        1.886e-02  1.019e+00  4.571e-02  0.412 0.679994    
CAD_history                                                3.407e-01  1.406e+00  3.736e-01  0.912 0.361863    
Stroke_history                                            -2.990e-01  7.415e-01  3.852e-01 -0.776 0.437536    
Peripheral.interv                                          2.667e-01  1.306e+00  4.307e-01  0.619 0.535753    
stenose0-49%                                              -2.044e+01  1.330e-09  1.406e+04 -0.001 0.998840    
stenose50-70%                                             -1.946e+01  3.552e-09  5.496e+03 -0.004 0.997175    
stenose70-90%                                             -2.985e+00  5.053e-02  1.196e+00 -2.495 0.012581 *  
stenose90-99%                                             -3.958e+00  1.911e-02  1.243e+00 -3.185 0.001448 ** 
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
hsCRP_plasma                                               6.384e-03  1.006e+00  1.939e-03  3.293 0.000991 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 4.120e-01  2.427e+00  0.200342    0.8472
Age                                                       1.107e+00  9.035e-01  1.046604    1.1705
Gendermale                                                3.073e+00  3.254e-01  1.133122    8.3358
Hypertension.compositeno                                  6.619e-01  1.511e+00  0.190237    2.3030
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    2.176e+00  4.595e-01  0.953914    4.9640
SmokerCurrentno                                           5.474e-01  1.827e+00  0.265933    1.1266
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.031e+00  9.700e-01  0.465386    2.2839
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    2.532e-01  3.949e+00  0.049753    1.2889
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  1.001e+00  9.994e-01  0.980861    1.0208
BMI                                                       1.019e+00  9.813e-01  0.931702    1.1146
CAD_history                                               1.406e+00  7.113e-01  0.675969    2.9240
Stroke_history                                            7.415e-01  1.349e+00  0.348535    1.5776
Peripheral.interv                                         1.306e+00  7.659e-01  0.561294    3.0374
stenose0-49%                                              1.330e-09  7.519e+08  0.000000       Inf
stenose50-70%                                             3.552e-09  2.815e+08  0.000000       Inf
stenose70-90%                                             5.053e-02  1.979e+01  0.004845    0.5270
stenose90-99%                                             1.911e-02  5.234e+01  0.001673    0.2182
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.006e+00  9.936e-01  1.002588    1.0102

Concordance= 0.785  (se = 0.039 )
Likelihood ratio test= 56.22  on 18 df,   p=8e-06
Wald test            = 36.47  on 18 df,   p=0.006
Score (logrank) test = 96.15  on 18 df,   p=1e-12


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.886767 
Standard error............: 0.367844 
Odds ratio (effect size)..: 0.412 
Lower 95% CI..............: 0.2 
Upper 95% CI..............: 0.847 
T-value...................: -2.410713 
P-value...................: 0.01592137 
Sample size in model......: 282 
Number of events..........: 37 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 618, number of events= 70 
   (1770 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061]  3.142e-01  1.369e+00  2.485e-01  1.264 0.206167    
Age                                                        6.558e-02  1.068e+00  1.756e-02  3.735 0.000188 ***
Gendermale                                                 1.155e+00  3.173e+00  3.397e-01  3.399 0.000676 ***
Hypertension.compositeno                                  -1.540e-01  8.573e-01  4.184e-01 -0.368 0.712809    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                     1.634e-01  1.177e+00  2.902e-01  0.563 0.573433    
SmokerCurrentno                                           -4.001e-01  6.702e-01  2.676e-01 -1.495 0.134858    
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           3.136e-01  1.368e+00  2.750e-01  1.141 0.254056    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     2.109e-01  1.235e+00  3.565e-01  0.592 0.554076    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -1.227e-02  9.878e-01  6.612e-03 -1.856 0.063401 .  
BMI                                                        3.692e-02  1.038e+00  3.542e-02  1.042 0.297219    
CAD_history                                                2.888e-03  1.003e+00  2.698e-01  0.011 0.991459    
Stroke_history                                            -1.418e-01  8.678e-01  2.673e-01 -0.531 0.595723    
Peripheral.interv                                          8.442e-01  2.326e+00  2.774e-01  3.043 0.002344 ** 
stenose0-49%                                              -1.473e+01  3.992e-07  2.091e+03 -0.007 0.994378    
stenose50-70%                                             -1.207e+00  2.991e-01  1.241e+00 -0.973 0.330584    
stenose70-90%                                             -3.726e-01  6.889e-01  1.031e+00 -0.361 0.717882    
stenose90-99%                                             -5.464e-01  5.790e-01  1.033e+00 -0.529 0.596974    
stenose100% (Occlusion)                                    1.423e+00  4.148e+00  1.474e+00  0.965 0.334614    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
hsCRP_plasma                                               1.720e-03  1.002e+00  6.275e-04  2.741 0.006131 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.369e+00  7.304e-01   0.84119     2.229
Age                                                       1.068e+00  9.365e-01   1.03166     1.105
Gendermale                                                3.173e+00  3.151e-01   1.63057     6.175
Hypertension.compositeno                                  8.573e-01  1.167e+00   0.37752     1.947
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.177e+00  8.493e-01   0.66674     2.079
SmokerCurrentno                                           6.702e-01  1.492e+00   0.39667     1.132
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.368e+00  7.308e-01   0.79826     2.346
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.235e+00  8.098e-01   0.61397     2.484
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.878e-01  1.012e+00   0.97508     1.001
BMI                                                       1.038e+00  9.638e-01   0.96803     1.112
CAD_history                                               1.003e+00  9.971e-01   0.59104     1.702
Stroke_history                                            8.678e-01  1.152e+00   0.51388     1.465
Peripheral.interv                                         2.326e+00  4.299e-01   1.35042     4.007
stenose0-49%                                              3.992e-07  2.505e+06   0.00000       Inf
stenose50-70%                                             2.991e-01  3.343e+00   0.02629     3.402
stenose70-90%                                             6.889e-01  1.452e+00   0.09126     5.201
stenose90-99%                                             5.790e-01  1.727e+00   0.07641     4.388
stenose100% (Occlusion)                                   4.148e+00  2.411e-01   0.23058    74.614
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.002e+00  9.983e-01   1.00049     1.003

Concordance= 0.72  (se = 0.032 )
Likelihood ratio test= 53.45  on 19 df,   p=4e-05
Wald test            = 44.44  on 19 df,   p=8e-04
Score (logrank) test = 49.32  on 19 df,   p=2e-04


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: 0.314211 
Standard error............: 0.24855 
Odds ratio (effect size)..: 1.369 
Lower 95% CI..............: 0.841 
Upper 95% CI..............: 2.229 
T-value...................: 1.264176 
P-value...................: 0.2061668 
Sample size in model......: 618 
Number of events..........: 70 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 623, number of events= 73 
   (1765 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]  4.692e-01  1.599e+00  2.551e-01  1.839 0.065846 .  
Age                                                        5.905e-02  1.061e+00  1.712e-02  3.448 0.000564 ***
Gendermale                                                 9.005e-01  2.461e+00  3.147e-01  2.862 0.004213 ** 
Hypertension.compositeno                                  -3.360e-02  9.670e-01  3.950e-01 -0.085 0.932211    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                     1.468e-01  1.158e+00  2.870e-01  0.511 0.609052    
SmokerCurrentno                                           -2.058e-01  8.140e-01  2.630e-01 -0.782 0.433941    
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           2.683e-01  1.308e+00  2.716e-01  0.988 0.323263    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.372e-01  1.147e+00  3.548e-01  0.387 0.698884    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -1.148e-02  9.886e-01  6.503e-03 -1.766 0.077473 .  
BMI                                                        4.213e-02  1.043e+00  3.544e-02  1.189 0.234513    
CAD_history                                                2.864e-02  1.029e+00  2.646e-01  0.108 0.913801    
Stroke_history                                            -1.326e-01  8.759e-01  2.609e-01 -0.508 0.611425    
Peripheral.interv                                          6.794e-01  1.973e+00  2.736e-01  2.484 0.013009 *  
stenose0-49%                                              -1.532e+01  2.214e-07  4.315e+03 -0.004 0.997166    
stenose50-70%                                             -1.435e+00  2.380e-01  1.249e+00 -1.149 0.250518    
stenose70-90%                                             -4.917e-01  6.116e-01  1.032e+00 -0.476 0.633801    
stenose90-99%                                             -7.206e-01  4.865e-01  1.037e+00 -0.695 0.487213    
stenose100% (Occlusion)                                    1.051e+00  2.862e+00  1.468e+00  0.716 0.473841    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.500e+01  3.050e-07  4.201e+03 -0.004 0.997151    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
hsCRP_plasma                                               1.724e-03  1.002e+00  6.220e-04  2.772 0.005580 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.599e+00  6.255e-01   0.96973     2.636
Age                                                       1.061e+00  9.427e-01   1.02581     1.097
Gendermale                                                2.461e+00  4.064e-01   1.32812     4.560
Hypertension.compositeno                                  9.670e-01  1.034e+00   0.44580     2.097
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.158e+00  8.635e-01   0.65990     2.032
SmokerCurrentno                                           8.140e-01  1.229e+00   0.48611     1.363
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.308e+00  7.647e-01   0.76791     2.227
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.147e+00  8.718e-01   0.57228     2.299
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.886e-01  1.012e+00   0.97606     1.001
BMI                                                       1.043e+00  9.587e-01   0.97304     1.118
CAD_history                                               1.029e+00  9.718e-01   0.61267     1.728
Stroke_history                                            8.759e-01  1.142e+00   0.52522     1.461
Peripheral.interv                                         1.973e+00  5.069e-01   1.15399     3.372
stenose0-49%                                              2.214e-07  4.518e+06   0.00000       Inf
stenose50-70%                                             2.380e-01  4.201e+00   0.02057     2.754
stenose70-90%                                             6.116e-01  1.635e+00   0.08087     4.625
stenose90-99%                                             4.865e-01  2.056e+00   0.06371     3.715
stenose100% (Occlusion)                                   2.862e+00  3.494e-01   0.16108    50.847
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             3.050e-07  3.279e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.002e+00  9.983e-01   1.00050     1.003

Concordance= 0.703  (se = 0.033 )
Likelihood ratio test= 47.73  on 20 df,   p=5e-04
Wald test            = 40.43  on 20 df,   p=0.004
Score (logrank) test = 45.02  on 20 df,   p=0.001


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.469189 
Standard error............: 0.255067 
Odds ratio (effect size)..: 1.599 
Lower 95% CI..............: 0.97 
Upper 95% CI..............: 2.636 
T-value...................: 1.839474 
P-value...................: 0.06584551 
Sample size in model......: 623 
Number of events..........: 73 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 642, number of events= 74 
   (1746 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148]  2.201e-01  1.246e+00  2.431e-01  0.905 0.365212    
Age                                                        5.764e-02  1.059e+00  1.683e-02  3.426 0.000613 ***
Gendermale                                                 8.810e-01  2.413e+00  3.078e-01  2.862 0.004206 ** 
Hypertension.compositeno                                  -5.189e-02  9.494e-01  3.941e-01 -0.132 0.895245    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                     1.785e-01  1.195e+00  2.823e-01  0.632 0.527074    
SmokerCurrentno                                           -2.384e-01  7.879e-01  2.611e-01 -0.913 0.361350    
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           3.541e-01  1.425e+00  2.664e-01  1.329 0.183735    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.218e-01  1.130e+00  3.526e-01  0.345 0.729815    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -1.364e-02  9.865e-01  6.516e-03 -2.093 0.036305 *  
BMI                                                        4.530e-02  1.046e+00  3.367e-02  1.345 0.178509    
CAD_history                                               -3.796e-02  9.628e-01  2.627e-01 -0.145 0.885093    
Stroke_history                                            -1.252e-01  8.823e-01  2.611e-01 -0.479 0.631705    
Peripheral.interv                                          6.843e-01  1.982e+00  2.733e-01  2.504 0.012277 *  
stenose0-49%                                              -1.558e+01  1.705e-07  3.356e+03 -0.005 0.996295    
stenose50-70%                                             -1.149e+00  3.168e-01  1.240e+00 -0.927 0.354080    
stenose70-90%                                             -2.584e-01  7.723e-01  1.033e+00 -0.250 0.802554    
stenose90-99%                                             -3.903e-01  6.769e-01  1.037e+00 -0.376 0.706558    
stenose100% (Occlusion)                                    1.598e+00  4.944e+00  1.484e+00  1.077 0.281380    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.497e+01  3.145e-07  4.137e+03 -0.004 0.997112    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
hsCRP_plasma                                               1.664e-03  1.002e+00  6.027e-04  2.761 0.005764 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.246e+00  8.025e-01   0.77391    2.0067
Age                                                       1.059e+00  9.440e-01   1.02497    1.0949
Gendermale                                                2.413e+00  4.144e-01   1.32013    4.4118
Hypertension.compositeno                                  9.494e-01  1.053e+00   0.43856    2.0554
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.195e+00  8.365e-01   0.68747    2.0789
SmokerCurrentno                                           7.879e-01  1.269e+00   0.47228    1.3145
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.425e+00  7.018e-01   0.84537    2.4017
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.130e+00  8.853e-01   0.56592    2.2544
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.865e-01  1.014e+00   0.97393    0.9991
BMI                                                       1.046e+00  9.557e-01   0.97952    1.1177
CAD_history                                               9.628e-01  1.039e+00   0.57535    1.6110
Stroke_history                                            8.823e-01  1.133e+00   0.52887    1.4721
Peripheral.interv                                         1.982e+00  5.045e-01   1.16030    3.3866
stenose0-49%                                              1.705e-07  5.866e+06   0.00000       Inf
stenose50-70%                                             3.168e-01  3.156e+00   0.02787    3.6020
stenose70-90%                                             7.723e-01  1.295e+00   0.10189    5.8538
stenose90-99%                                             6.769e-01  1.477e+00   0.08874    5.1628
stenose100% (Occlusion)                                   4.944e+00  2.023e-01   0.26991   90.5629
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             3.145e-07  3.179e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.002e+00  9.983e-01   1.00048    1.0028

Concordance= 0.701  (se = 0.032 )
Likelihood ratio test= 46.5  on 20 df,   p=7e-04
Wald test            = 39.27  on 20 df,   p=0.006
Score (logrank) test = 43.7  on 20 df,   p=0.002


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.220083 
Standard error............: 0.243057 
Odds ratio (effect size)..: 1.246 
Lower 95% CI..............: 0.774 
Upper 95% CI..............: 2.007 
T-value...................: 0.905479 
P-value...................: 0.3652118 
Sample size in model......: 642 
Number of events..........: 74 
* Analyzing the effect of plaque proteins on [epstroke.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 268, number of events= 16 
   (2120 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -6.343e-01  5.303e-01  5.812e-01 -1.091  0.27513   
Age                                                        1.118e-01  1.118e+00  4.212e-02  2.655  0.00793 **
Gendermale                                                 2.721e-01  1.313e+00  6.816e-01  0.399  0.68975   
Hypertension.compositeno                                  -3.726e-01  6.889e-01  8.437e-01 -0.442  0.65875   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     2.760e-01  1.318e+00  7.372e-01  0.374  0.70815   
SmokerCurrentno                                           -3.276e-01  7.206e-01  5.847e-01 -0.560  0.57526   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                           6.236e-02  1.064e+00  6.046e-01  0.103  0.91784   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                    -6.084e-01  5.442e-01  1.205e+00 -0.505  0.61357   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                   1.413e-02  1.014e+00  1.725e-02  0.819  0.41262   
BMI                                                        2.352e-02  1.024e+00  7.812e-02  0.301  0.76336   
CAD_history                                                1.104e-01  1.117e+00  6.060e-01  0.182  0.85540   
Stroke_history                                             8.518e-03  1.009e+00  5.725e-01  0.015  0.98813   
Peripheral.interv                                         -4.181e-01  6.583e-01  7.891e-01 -0.530  0.59618   
stenose0-49%                                              -1.949e+01  3.441e-09  1.647e+04 -0.001  0.99906   
stenose50-70%                                             -1.886e+01  6.418e-09  6.979e+03 -0.003  0.99784   
stenose70-90%                                             -2.966e+00  5.151e-02  1.357e+00 -2.186  0.02882 * 
stenose90-99%                                             -3.589e+00  2.763e-02  1.406e+00 -2.553  0.01068 * 
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
hsCRP_plasma                                               4.996e-03  1.005e+00  1.756e-03  2.845  0.00444 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 5.303e-01  1.886e+00  0.169747    1.6568
Age                                                       1.118e+00  8.942e-01  1.029711    1.2146
Gendermale                                                1.313e+00  7.618e-01  0.345159    4.9923
Hypertension.compositeno                                  6.889e-01  1.452e+00  0.131836    3.6002
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.318e+00  7.588e-01  0.310717    5.5889
SmokerCurrentno                                           7.206e-01  1.388e+00  0.229109    2.2667
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.064e+00  9.395e-01  0.325439    3.4810
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    5.442e-01  1.837e+00  0.051321    5.7712
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  1.014e+00  9.860e-01  0.980521    1.0491
BMI                                                       1.024e+00  9.768e-01  0.878449    1.1932
CAD_history                                               1.117e+00  8.955e-01  0.340531    3.6623
Stroke_history                                            1.009e+00  9.915e-01  0.328392    3.0975
Peripheral.interv                                         6.583e-01  1.519e+00  0.140194    3.0909
stenose0-49%                                              3.441e-09  2.907e+08  0.000000       Inf
stenose50-70%                                             6.418e-09  1.558e+08  0.000000       Inf
stenose70-90%                                             5.151e-02  1.941e+01  0.003605    0.7360
stenose90-99%                                             2.763e-02  3.620e+01  0.001757    0.4344
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.005e+00  9.950e-01  1.001556    1.0085

Concordance= 0.775  (se = 0.062 )
Likelihood ratio test= 24.03  on 18 df,   p=0.2
Wald test            = 13.98  on 18 df,   p=0.7
Score (logrank) test = 69.62  on 18 df,   p=5e-08


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: IL6_rank 
Effect size...............: -0.634292 
Standard error............: 0.581212 
Odds ratio (effect size)..: 0.53 
Lower 95% CI..............: 0.17 
Upper 95% CI..............: 1.657 
T-value...................: -1.091327 
P-value...................: 0.2751288 
Sample size in model......: 268 
Number of events..........: 16 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 282, number of events= 17 
   (2106 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -1.353e+00  2.584e-01  5.944e-01 -2.276  0.02282 * 
Age                                                        7.559e-02  1.079e+00  3.884e-02  1.946  0.05162 . 
Gendermale                                                 6.031e-01  1.828e+00  6.803e-01  0.886  0.37539   
Hypertension.compositeno                                   8.003e-03  1.008e+00  8.095e-01  0.010  0.99211   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                    -2.550e-01  7.749e-01  7.332e-01 -0.348  0.72802   
SmokerCurrentno                                           -1.702e-01  8.435e-01  5.799e-01 -0.293  0.76919   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                           2.128e-02  1.022e+00  5.846e-01  0.036  0.97097   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                    -1.196e+00  3.025e-01  1.332e+00 -0.898  0.36918   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                   1.406e-02  1.014e+00  1.608e-02  0.874  0.38201   
BMI                                                        7.504e-02  1.078e+00  6.347e-02  1.182  0.23709   
CAD_history                                               -5.998e-02  9.418e-01  5.898e-01 -0.102  0.91899   
Stroke_history                                            -2.604e-01  7.707e-01  5.579e-01 -0.467  0.64066   
Peripheral.interv                                         -5.122e-01  5.992e-01  7.849e-01 -0.652  0.51409   
stenose0-49%                                              -1.930e+01  4.153e-09  1.660e+04 -0.001  0.99907   
stenose50-70%                                             -1.950e+01  3.405e-09  6.144e+03 -0.003  0.99747   
stenose70-90%                                             -3.365e+00  3.456e-02  1.443e+00 -2.333  0.01967 * 
stenose90-99%                                             -3.694e+00  2.487e-02  1.447e+00 -2.552  0.01070 * 
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
hsCRP_plasma                                               5.712e-03  1.006e+00  1.759e-03  3.248  0.00116 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 2.584e-01  3.870e+00  0.080610    0.8285
Age                                                       1.079e+00  9.272e-01  0.999468    1.1638
Gendermale                                                1.828e+00  5.471e-01  0.481725    6.9347
Hypertension.compositeno                                  1.008e+00  9.920e-01  0.206272    4.9262
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    7.749e-01  1.290e+00  0.184132    3.2613
SmokerCurrentno                                           8.435e-01  1.186e+00  0.270693    2.6286
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.022e+00  9.789e-01  0.324801    3.2126
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    3.025e-01  3.306e+00  0.022246    4.1127
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  1.014e+00  9.860e-01  0.982693    1.0466
BMI                                                       1.078e+00  9.277e-01  0.951838    1.2207
CAD_history                                               9.418e-01  1.062e+00  0.296414    2.9923
Stroke_history                                            7.707e-01  1.297e+00  0.258260    2.3002
Peripheral.interv                                         5.992e-01  1.669e+00  0.128657    2.7907
stenose0-49%                                              4.153e-09  2.408e+08  0.000000       Inf
stenose50-70%                                             3.405e-09  2.937e+08  0.000000       Inf
stenose70-90%                                             3.456e-02  2.894e+01  0.002045    0.5841
stenose90-99%                                             2.487e-02  4.022e+01  0.001457    0.4243
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.006e+00  9.943e-01  1.002267    1.0092

Concordance= 0.778  (se = 0.044 )
Likelihood ratio test= 27.05  on 18 df,   p=0.08
Wald test            = 17.77  on 18 df,   p=0.5
Score (logrank) test = 74.01  on 18 df,   p=9e-09


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_rank 
Effect size...............: -1.353138 
Standard error............: 0.594398 
Odds ratio (effect size)..: 0.258 
Lower 95% CI..............: 0.081 
Upper 95% CI..............: 0.829 
T-value...................: -2.276484 
P-value...................: 0.02281707 
Sample size in model......: 282 
Number of events..........: 17 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 618, number of events= 36 
   (1770 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] -7.706e-02  9.258e-01  3.574e-01 -0.216 0.829293    
Age                                                        1.048e-01  1.110e+00  2.521e-02  4.155 3.25e-05 ***
Gendermale                                                 1.112e+00  3.040e+00  4.667e-01  2.382 0.017222 *  
Hypertension.compositeno                                   2.472e-01  1.280e+00  4.770e-01  0.518 0.604307    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                     1.545e-01  1.167e+00  4.216e-01  0.366 0.714022    
SmokerCurrentno                                            5.431e-02  1.056e+00  4.023e-01  0.135 0.892602    
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           5.182e-01  1.679e+00  3.595e-01  1.442 0.149407    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     8.201e-02  1.085e+00  5.079e-01  0.161 0.871733    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                   6.588e-03  1.007e+00  9.032e-03  0.729 0.465734    
BMI                                                        1.056e-01  1.111e+00  4.614e-02  2.288 0.022120 *  
CAD_history                                               -9.445e-01  3.889e-01  4.605e-01 -2.051 0.040259 *  
Stroke_history                                             1.343e-01  1.144e+00  3.670e-01  0.366 0.714459    
Peripheral.interv                                          9.068e-01  2.476e+00  4.014e-01  2.259 0.023885 *  
stenose0-49%                                              -2.542e-01  7.755e-01  7.243e+03  0.000 0.999972    
stenose50-70%                                              1.562e+01  6.097e+06  4.571e+03  0.003 0.997273    
stenose70-90%                                              1.597e+01  8.649e+06  4.571e+03  0.003 0.997212    
stenose90-99%                                              1.576e+01  6.960e+06  4.571e+03  0.003 0.997250    
stenose100% (Occlusion)                                    1.859e+01  1.188e+08  4.571e+03  0.004 0.996755    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
hsCRP_plasma                                               2.753e-03  1.003e+00  7.714e-04  3.569 0.000359 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 9.258e-01  1.080e+00    0.4595    1.8653
Age                                                       1.110e+00  9.005e-01    1.0569    1.1667
Gendermale                                                3.040e+00  3.290e-01    1.2177    7.5878
Hypertension.compositeno                                  1.280e+00  7.810e-01    0.5027    3.2615
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.167e+00  8.569e-01    0.5108    2.6665
SmokerCurrentno                                           1.056e+00  9.471e-01    0.4799    2.3227
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.679e+00  5.956e-01    0.8300    3.3966
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.085e+00  9.213e-01    0.4011    2.9374
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  1.007e+00  9.934e-01    0.9889    1.0246
BMI                                                       1.111e+00  8.998e-01    1.0153    1.2165
CAD_history                                               3.889e-01  2.572e+00    0.1577    0.9589
Stroke_history                                            1.144e+00  8.744e-01    0.5571    2.3480
Peripheral.interv                                         2.476e+00  4.038e-01    1.1275    5.4385
stenose0-49%                                              7.755e-01  1.289e+00    0.0000       Inf
stenose50-70%                                             6.097e+06  1.640e-07    0.0000       Inf
stenose70-90%                                             8.649e+06  1.156e-07    0.0000       Inf
stenose90-99%                                             6.960e+06  1.437e-07    0.0000       Inf
stenose100% (Occlusion)                                   1.188e+08  8.416e-09    0.0000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.003e+00  9.973e-01    1.0012    1.0043

Concordance= 0.751  (se = 0.045 )
Likelihood ratio test= 41.44  on 19 df,   p=0.002
Wald test            = 34.59  on 19 df,   p=0.02
Score (logrank) test = 40.43  on 19 df,   p=0.003


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: -0.077056 
Standard error............: 0.357389 
Odds ratio (effect size)..: 0.926 
Lower 95% CI..............: 0.46 
Upper 95% CI..............: 1.865 
T-value...................: -0.215609 
P-value...................: 0.8292928 
Sample size in model......: 618 
Number of events..........: 36 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 623, number of events= 38 
   (1765 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]  1.328e-01  1.142e+00  3.479e-01  0.382 0.702622    
Age                                                        9.242e-02  1.097e+00  2.385e-02  3.875 0.000106 ***
Gendermale                                                 9.010e-01  2.462e+00  4.339e-01  2.076 0.037867 *  
Hypertension.compositeno                                   3.920e-01  1.480e+00  4.465e-01  0.878 0.380042    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                     8.893e-02  1.093e+00  4.131e-01  0.215 0.829560    
SmokerCurrentno                                            2.213e-01  1.248e+00  3.964e-01  0.558 0.576680    
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           5.507e-01  1.734e+00  3.524e-01  1.563 0.118089    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     6.852e-03  1.007e+00  5.053e-01  0.014 0.989181    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                   8.250e-03  1.008e+00  8.828e-03  0.935 0.350035    
BMI                                                        1.240e-01  1.132e+00  4.815e-02  2.576 0.009999 ** 
CAD_history                                               -7.710e-01  4.625e-01  4.351e-01 -1.772 0.076349 .  
Stroke_history                                             1.645e-01  1.179e+00  3.523e-01  0.467 0.640471    
Peripheral.interv                                          8.030e-01  2.232e+00  3.973e-01  2.021 0.043271 *  
stenose0-49%                                               7.960e-01  2.217e+00  1.113e+04  0.000 0.999943    
stenose50-70%                                              1.592e+01  8.239e+06  5.452e+03  0.003 0.997669    
stenose70-90%                                              1.640e+01  1.321e+07  5.452e+03  0.003 0.997600    
stenose90-99%                                              1.608e+01  9.642e+06  5.452e+03  0.003 0.997646    
stenose100% (Occlusion)                                    1.888e+01  1.579e+08  5.452e+03  0.003 0.997237    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                              6.571e-01  1.929e+00  1.157e+04  0.000 0.999955    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
hsCRP_plasma                                               2.565e-03  1.003e+00  7.306e-04  3.510 0.000448 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.142e+00  8.756e-01    0.5775     2.259
Age                                                       1.097e+00  9.117e-01    1.0467     1.149
Gendermale                                                2.462e+00  4.062e-01    1.0518     5.763
Hypertension.compositeno                                  1.480e+00  6.757e-01    0.6168     3.551
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.093e+00  9.149e-01    0.4864     2.456
SmokerCurrentno                                           1.248e+00  8.015e-01    0.5737     2.714
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.734e+00  5.766e-01    0.8694     3.460
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.007e+00  9.932e-01    0.3740     2.711
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  1.008e+00  9.918e-01    0.9910     1.026
BMI                                                       1.132e+00  8.834e-01    1.0301     1.244
CAD_history                                               4.625e-01  2.162e+00    0.1972     1.085
Stroke_history                                            1.179e+00  8.483e-01    0.5910     2.351
Peripheral.interv                                         2.232e+00  4.480e-01    1.0246     4.863
stenose0-49%                                              2.217e+00  4.511e-01    0.0000       Inf
stenose50-70%                                             8.239e+06  1.214e-07    0.0000       Inf
stenose70-90%                                             1.321e+07  7.571e-08    0.0000       Inf
stenose90-99%                                             9.642e+06  1.037e-07    0.0000       Inf
stenose100% (Occlusion)                                   1.579e+08  6.333e-09    0.0000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             1.929e+00  5.183e-01    0.0000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.003e+00  9.974e-01    1.0011     1.004

Concordance= 0.74  (se = 0.045 )
Likelihood ratio test= 39.66  on 20 df,   p=0.006
Wald test            = 33.6  on 20 df,   p=0.03
Score (logrank) test = 39.52  on 20 df,   p=0.006


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.132838 
Standard error............: 0.347941 
Odds ratio (effect size)..: 1.142 
Lower 95% CI..............: 0.577 
Upper 95% CI..............: 2.259 
T-value...................: 0.381784 
P-value...................: 0.7026217 
Sample size in model......: 623 
Number of events..........: 38 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 642, number of events= 39 
   (1746 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148]  1.603e-01  1.174e+00  3.445e-01  0.465 0.641655    
Age                                                        9.215e-02  1.097e+00  2.345e-02  3.929 8.53e-05 ***
Gendermale                                                 8.195e-01  2.269e+00  4.185e-01  1.958 0.050198 .  
Hypertension.compositeno                                   3.903e-01  1.477e+00  4.448e-01  0.877 0.380225    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                     1.920e-01  1.212e+00  4.029e-01  0.477 0.633640    
SmokerCurrentno                                            2.378e-01  1.268e+00  3.930e-01  0.605 0.545145    
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           5.567e-01  1.745e+00  3.438e-01  1.619 0.105420    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                    -3.158e-02  9.689e-01  5.040e-01 -0.063 0.950038    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                   5.474e-03  1.005e+00  8.808e-03  0.621 0.534299    
BMI                                                        1.111e-01  1.118e+00  4.235e-02  2.624 0.008682 ** 
CAD_history                                               -8.088e-01  4.454e-01  4.280e-01 -1.890 0.058786 .  
Stroke_history                                             1.279e-01  1.136e+00  3.524e-01  0.363 0.716665    
Peripheral.interv                                          7.314e-01  2.078e+00  3.961e-01  1.846 0.064855 .  
stenose0-49%                                              -1.147e-02  9.886e-01  7.076e+03  0.000 0.999999    
stenose50-70%                                              1.553e+01  5.543e+06  4.362e+03  0.004 0.997160    
stenose70-90%                                              1.601e+01  8.936e+06  4.362e+03  0.004 0.997072    
stenose90-99%                                              1.581e+01  7.344e+06  4.362e+03  0.004 0.997108    
stenose100% (Occlusion)                                    1.865e+01  1.253e+08  4.362e+03  0.004 0.996589    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                              8.436e-01  2.325e+00  8.528e+03  0.000 0.999921    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
hsCRP_plasma                                               2.534e-03  1.003e+00  7.256e-04  3.492 0.000479 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.174e+00  8.519e-01    0.5976     2.306
Age                                                       1.097e+00  9.120e-01    1.0473     1.148
Gendermale                                                2.269e+00  4.407e-01    0.9993     5.154
Hypertension.compositeno                                  1.477e+00  6.768e-01    0.6178     3.533
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.212e+00  8.253e-01    0.5501     2.669
SmokerCurrentno                                           1.268e+00  7.884e-01    0.5871     2.740
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.745e+00  5.731e-01    0.8894     3.423
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    9.689e-01  1.032e+00    0.3608     2.602
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  1.005e+00  9.945e-01    0.9883     1.023
BMI                                                       1.118e+00  8.948e-01    1.0285     1.214
CAD_history                                               4.454e-01  2.245e+00    0.1925     1.030
Stroke_history                                            1.136e+00  8.799e-01    0.5696     2.267
Peripheral.interv                                         2.078e+00  4.812e-01    0.9560     4.517
stenose0-49%                                              9.886e-01  1.012e+00    0.0000       Inf
stenose50-70%                                             5.543e+06  1.804e-07    0.0000       Inf
stenose70-90%                                             8.936e+06  1.119e-07    0.0000       Inf
stenose90-99%                                             7.344e+06  1.362e-07    0.0000       Inf
stenose100% (Occlusion)                                   1.253e+08  7.980e-09    0.0000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.325e+00  4.302e-01    0.0000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.003e+00  9.975e-01    1.0011     1.004

Concordance= 0.743  (se = 0.043 )
Likelihood ratio test= 39.62  on 20 df,   p=0.006
Wald test            = 33.61  on 20 df,   p=0.03
Score (logrank) test = 39.6  on 20 df,   p=0.006


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.160313 
Standard error............: 0.344474 
Odds ratio (effect size)..: 1.174 
Lower 95% CI..............: 0.598 
Upper 95% CI..............: 2.306 
T-value...................: 0.465386 
P-value...................: 0.6416551 
Sample size in model......: 642 
Number of events..........: 39 
* Analyzing the effect of plaque proteins on [epcoronary.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 268, number of events= 26 
   (2120 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -1.951e-01  8.228e-01  4.190e-01 -0.466  0.64150   
Age                                                        8.561e-02  1.089e+00  3.252e-02  2.633  0.00847 **
Gendermale                                                 5.439e-01  1.723e+00  5.294e-01  1.027  0.30427   
Hypertension.compositeno                                  -5.772e-01  5.614e-01  7.702e-01 -0.750  0.45355   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     6.536e-01  1.922e+00  4.900e-01  1.334  0.18221   
SmokerCurrentno                                           -4.073e-01  6.655e-01  4.363e-01 -0.934  0.35055   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                          -7.107e-01  4.913e-01  5.438e-01 -1.307  0.19126   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     8.448e-02  1.088e+00  7.544e-01  0.112  0.91085   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                   4.736e-03  1.005e+00  1.300e-02  0.364  0.71566   
BMI                                                       -2.461e-03  9.975e-01  5.606e-02 -0.044  0.96498   
CAD_history                                                6.008e-01  1.824e+00  4.462e-01  1.346  0.17816   
Stroke_history                                            -4.277e-01  6.520e-01  4.858e-01 -0.880  0.37866   
Peripheral.interv                                          2.224e-01  1.249e+00  4.758e-01  0.467  0.64021   
stenose0-49%                                              -1.167e+00  3.114e-01  2.163e+04  0.000  0.99996   
stenose50-70%                                             -7.764e-02  9.253e-01  1.211e+04  0.000  0.99999   
stenose70-90%                                              1.676e+01  1.898e+07  9.270e+03  0.002  0.99856   
stenose90-99%                                              1.607e+01  9.502e+06  9.270e+03  0.002  0.99862   
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
hsCRP_plasma                                               1.489e-03  1.001e+00  1.290e-03  1.154  0.24864   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 8.228e-01  1.215e+00    0.3619     1.870
Age                                                       1.089e+00  9.180e-01    1.0221     1.161
Gendermale                                                1.723e+00  5.805e-01    0.6103     4.862
Hypertension.compositeno                                  5.614e-01  1.781e+00    0.1241     2.540
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.922e+00  5.202e-01    0.7359     5.022
SmokerCurrentno                                           6.655e-01  1.503e+00    0.2830     1.565
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          4.913e-01  2.035e+00    0.1692     1.426
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.088e+00  9.190e-01    0.2480     4.774
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  1.005e+00  9.953e-01    0.9795     1.031
BMI                                                       9.975e-01  1.002e+00    0.8937     1.113
CAD_history                                               1.824e+00  5.484e-01    0.7605     4.373
Stroke_history                                            6.520e-01  1.534e+00    0.2516     1.690
Peripheral.interv                                         1.249e+00  8.006e-01    0.4916     3.174
stenose0-49%                                              3.114e-01  3.211e+00    0.0000       Inf
stenose50-70%                                             9.253e-01  1.081e+00    0.0000       Inf
stenose70-90%                                             1.898e+07  5.269e-08    0.0000       Inf
stenose90-99%                                             9.502e+06  1.052e-07    0.0000       Inf
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.001e+00  9.985e-01    0.9990     1.004

Concordance= 0.748  (se = 0.04 )
Likelihood ratio test= 22.38  on 18 df,   p=0.2
Wald test            = 16.91  on 18 df,   p=0.5
Score (logrank) test = 20.49  on 18 df,   p=0.3


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: IL6_rank 
Effect size...............: -0.195076 
Standard error............: 0.418981 
Odds ratio (effect size)..: 0.823 
Lower 95% CI..............: 0.362 
Upper 95% CI..............: 1.87 
T-value...................: -0.465596 
P-value...................: 0.6415048 
Sample size in model......: 268 
Number of events..........: 26 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 282, number of events= 26 
   (2106 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635]  2.502e-01  1.284e+00  4.203e-01  0.595  0.55159   
Age                                                        8.205e-02  1.086e+00  3.145e-02  2.609  0.00909 **
Gendermale                                                 4.205e-01  1.523e+00  5.317e-01  0.791  0.42897   
Hypertension.compositeno                                  -5.981e-01  5.498e-01  7.660e-01 -0.781  0.43490   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     8.161e-01  2.262e+00  4.796e-01  1.701  0.08887 . 
SmokerCurrentno                                           -6.407e-01  5.269e-01  4.264e-01 -1.503  0.13292   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                          -5.710e-01  5.650e-01  5.466e-01 -1.045  0.29618   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                    -2.510e-01  7.780e-01  7.633e-01 -0.329  0.74230   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                   5.054e-03  1.005e+00  1.260e-02  0.401  0.68826   
BMI                                                        3.259e-03  1.003e+00  5.345e-02  0.061  0.95137   
CAD_history                                                5.956e-01  1.814e+00  4.438e-01  1.342  0.17962   
Stroke_history                                            -3.392e-01  7.124e-01  4.700e-01 -0.722  0.47055   
Peripheral.interv                                          5.946e-01  1.812e+00  4.567e-01  1.302  0.19292   
stenose0-49%                                              -1.809e+00  1.638e-01  1.440e+04  0.000  0.99990   
stenose50-70%                                             -3.731e-01  6.886e-01  8.607e+03  0.000  0.99997   
stenose70-90%                                              1.555e+01  5.656e+06  7.113e+03  0.002  0.99826   
stenose90-99%                                              1.486e+01  2.855e+06  7.113e+03  0.002  0.99833   
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
hsCRP_plasma                                               1.423e-03  1.001e+00  1.312e-03  1.084  0.27828   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 1.284e+00  7.786e-01    0.5635     2.927
Age                                                       1.086e+00  9.212e-01    1.0206     1.155
Gendermale                                                1.523e+00  6.567e-01    0.5371     4.317
Hypertension.compositeno                                  5.498e-01  1.819e+00    0.1225     2.468
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    2.262e+00  4.422e-01    0.8834     5.790
SmokerCurrentno                                           5.269e-01  1.898e+00    0.2285     1.215
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          5.650e-01  1.770e+00    0.1935     1.649
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    7.780e-01  1.285e+00    0.1743     3.473
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  1.005e+00  9.950e-01    0.9806     1.030
BMI                                                       1.003e+00  9.967e-01    0.9035     1.114
CAD_history                                               1.814e+00  5.512e-01    0.7601     4.330
Stroke_history                                            7.124e-01  1.404e+00    0.2835     1.790
Peripheral.interv                                         1.812e+00  5.518e-01    0.7405     4.436
stenose0-49%                                              1.638e-01  6.104e+00    0.0000       Inf
stenose50-70%                                             6.886e-01  1.452e+00    0.0000       Inf
stenose70-90%                                             5.656e+06  1.768e-07    0.0000       Inf
stenose90-99%                                             2.855e+06  3.502e-07    0.0000       Inf
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.001e+00  9.986e-01    0.9989     1.004

Concordance= 0.743  (se = 0.042 )
Likelihood ratio test= 22.86  on 18 df,   p=0.2
Wald test            = 18.54  on 18 df,   p=0.4
Score (logrank) test = 22.18  on 18 df,   p=0.2


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_rank 
Effect size...............: 0.250218 
Standard error............: 0.420272 
Odds ratio (effect size)..: 1.284 
Lower 95% CI..............: 0.564 
Upper 95% CI..............: 2.927 
T-value...................: 0.595372 
P-value...................: 0.5515948 
Sample size in model......: 282 
Number of events..........: 26 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 618, number of events= 47 
   (1770 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061]  3.396e-01  1.404e+00  3.052e-01  1.113 0.265852    
Age                                                       -5.270e-03  9.947e-01  2.040e-02 -0.258 0.796187    
Gendermale                                                 9.025e-01  2.466e+00  4.015e-01  2.248 0.024587 *  
Hypertension.compositeno                                  -9.062e-01  4.040e-01  7.423e-01 -1.221 0.222132    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                     1.538e-01  1.166e+00  3.464e-01  0.444 0.657127    
SmokerCurrentno                                           -7.453e-01  4.746e-01  3.139e-01 -2.374 0.017580 *  
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                          -8.568e-02  9.179e-01  3.868e-01 -0.222 0.824695    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     3.711e-02  1.038e+00  4.917e-01  0.075 0.939834    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -1.146e-02  9.886e-01  8.202e-03 -1.397 0.162283    
BMI                                                       -1.389e-02  9.862e-01  4.262e-02 -0.326 0.744519    
CAD_history                                                1.124e+00  3.076e+00  3.222e-01  3.488 0.000487 ***
Stroke_history                                             3.471e-02  1.035e+00  3.271e-01  0.106 0.915502    
Peripheral.interv                                          4.500e-01  1.568e+00  3.286e-01  1.369 0.170844    
stenose0-49%                                              -1.835e+01  1.077e-08  1.248e+04 -0.001 0.998827    
stenose50-70%                                             -1.831e+01  1.113e-08  3.039e+03 -0.006 0.995192    
stenose70-90%                                             -1.290e+00  2.754e-01  1.053e+00 -1.225 0.220712    
stenose90-99%                                             -1.479e+00  2.278e-01  1.054e+00 -1.403 0.160577    
stenose100% (Occlusion)                                   -1.868e+01  7.697e-09  9.605e+03 -0.002 0.998448    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
hsCRP_plasma                                               7.612e-05  1.000e+00  9.969e-04  0.076 0.939134    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.404e+00  7.121e-01   0.77214     2.554
Age                                                       9.947e-01  1.005e+00   0.95575     1.035
Gendermale                                                2.466e+00  4.056e-01   1.12252     5.416
Hypertension.compositeno                                  4.040e-01  2.475e+00   0.09432     1.731
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.166e+00  8.575e-01   0.59148     2.299
SmokerCurrentno                                           4.746e-01  2.107e+00   0.25651     0.878
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          9.179e-01  1.089e+00   0.43006     1.959
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.038e+00  9.636e-01   0.39592     2.720
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.886e-01  1.012e+00   0.97284     1.005
BMI                                                       9.862e-01  1.014e+00   0.90716     1.072
CAD_history                                               3.076e+00  3.251e-01   1.63600     5.785
Stroke_history                                            1.035e+00  9.659e-01   0.54531     1.966
Peripheral.interv                                         1.568e+00  6.376e-01   0.82363     2.986
stenose0-49%                                              1.077e-08  9.283e+07   0.00000       Inf
stenose50-70%                                             1.113e-08  8.988e+07   0.00000       Inf
stenose70-90%                                             2.754e-01  3.631e+00   0.03496     2.169
stenose90-99%                                             2.278e-01  4.389e+00   0.02886     1.799
stenose100% (Occlusion)                                   7.697e-09  1.299e+08   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.000e+00  9.999e-01   0.99812     1.002

Concordance= 0.763  (se = 0.034 )
Likelihood ratio test= 43.68  on 19 df,   p=0.001
Wald test            = 30.52  on 19 df,   p=0.05
Score (logrank) test = 42.26  on 19 df,   p=0.002


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: 0.339572 
Standard error............: 0.305188 
Odds ratio (effect size)..: 1.404 
Lower 95% CI..............: 0.772 
Upper 95% CI..............: 2.554 
T-value...................: 1.112665 
P-value...................: 0.2658522 
Sample size in model......: 618 
Number of events..........: 47 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 623, number of events= 48 
   (1765 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]  6.164e-01  1.852e+00  3.156e-01  1.953  0.05081 . 
Age                                                       -2.033e-03  9.980e-01  2.005e-02 -0.101  0.91925   
Gendermale                                                 6.907e-01  1.995e+00  3.815e-01  1.811  0.07018 . 
Hypertension.compositeno                                  -9.056e-01  4.043e-01  7.411e-01 -1.222  0.22172   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     1.721e-01  1.188e+00  3.477e-01  0.495  0.62060   
SmokerCurrentno                                           -5.742e-01  5.632e-01  3.109e-01 -1.847  0.06479 . 
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                          -1.747e-01  8.397e-01  3.898e-01 -0.448  0.65406   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     1.668e-02  1.017e+00  4.932e-01  0.034  0.97302   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -1.222e-02  9.879e-01  8.077e-03 -1.513  0.13033   
BMI                                                       -2.167e-02  9.786e-01  4.284e-02 -0.506  0.61295   
CAD_history                                                1.029e+00  2.799e+00  3.163e-01  3.253  0.00114 **
Stroke_history                                            -1.088e-02  9.892e-01  3.259e-01 -0.033  0.97337   
Peripheral.interv                                          3.127e-01  1.367e+00  3.270e-01  0.956  0.33893   
stenose0-49%                                              -1.842e+01  1.005e-08  1.484e+04 -0.001  0.99901   
stenose50-70%                                             -1.838e+01  1.044e-08  2.972e+03 -0.006  0.99507   
stenose70-90%                                             -1.422e+00  2.413e-01  1.061e+00 -1.340  0.18020   
stenose90-99%                                             -1.576e+00  2.067e-01  1.063e+00 -1.483  0.13801   
stenose100% (Occlusion)                                   -1.873e+01  7.313e-09  8.875e+03 -0.002  0.99832   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                             -1.827e+01  1.165e-08  1.496e+04 -0.001  0.99903   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
hsCRP_plasma                                               2.533e-04  1.000e+00  1.013e-03  0.250  0.80256   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.852e+00  5.399e-01   0.99784     3.438
Age                                                       9.980e-01  1.002e+00   0.95951     1.038
Gendermale                                                1.995e+00  5.012e-01   0.94468     4.214
Hypertension.compositeno                                  4.043e-01  2.473e+00   0.09461     1.728
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.188e+00  8.419e-01   0.60085     2.348
SmokerCurrentno                                           5.632e-01  1.776e+00   0.30619     1.036
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          8.397e-01  1.191e+00   0.39114     1.803
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.017e+00  9.835e-01   0.38674     2.673
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.879e-01  1.012e+00   0.97234     1.004
BMI                                                       9.786e-01  1.022e+00   0.89976     1.064
CAD_history                                               2.799e+00  3.573e-01   1.50554     5.202
Stroke_history                                            9.892e-01  1.011e+00   0.52226     1.874
Peripheral.interv                                         1.367e+00  7.315e-01   0.72024     2.595
stenose0-49%                                              1.005e-08  9.949e+07   0.00000       Inf
stenose50-70%                                             1.044e-08  9.582e+07   0.00000       Inf
stenose70-90%                                             2.413e-01  4.145e+00   0.03016     1.930
stenose90-99%                                             2.067e-01  4.837e+00   0.02575     1.660
stenose100% (Occlusion)                                   7.313e-09  1.367e+08   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             1.165e-08  8.583e+07   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.000e+00  9.997e-01   0.99827     1.002

Concordance= 0.754  (se = 0.037 )
Likelihood ratio test= 41.86  on 20 df,   p=0.003
Wald test            = 29.23  on 20 df,   p=0.08
Score (logrank) test = 40.5  on 20 df,   p=0.004


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.616391 
Standard error............: 0.315596 
Odds ratio (effect size)..: 1.852 
Lower 95% CI..............: 0.998 
Upper 95% CI..............: 3.438 
T-value...................: 1.953101 
P-value...................: 0.05080762 
Sample size in model......: 623 
Number of events..........: 48 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 642, number of events= 48 
   (1746 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148]  1.453e-01  1.156e+00  2.985e-01  0.487  0.62654   
Age                                                       -6.351e-03  9.937e-01  1.984e-02 -0.320  0.74890   
Gendermale                                                 7.713e-01  2.163e+00  3.825e-01  2.017  0.04375 * 
Hypertension.compositeno                                  -9.429e-01  3.895e-01  7.405e-01 -1.273  0.20287   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     9.828e-02  1.103e+00  3.449e-01  0.285  0.77568   
SmokerCurrentno                                           -6.301e-01  5.326e-01  3.087e-01 -2.041  0.04125 * 
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                          -4.514e-02  9.559e-01  3.864e-01 -0.117  0.90701   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     2.968e-02  1.030e+00  4.891e-01  0.061  0.95161   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -1.446e-02  9.856e-01  8.132e-03 -1.778  0.07534 . 
BMI                                                       -1.959e-02  9.806e-01  4.266e-02 -0.459  0.64600   
CAD_history                                                1.013e+00  2.755e+00  3.177e-01  3.190  0.00142 **
Stroke_history                                             3.953e-02  1.040e+00  3.279e-01  0.121  0.90406   
Peripheral.interv                                          3.587e-01  1.431e+00  3.264e-01  1.099  0.27180   
stenose0-49%                                              -1.820e+01  1.243e-08  1.223e+04 -0.001  0.99881   
stenose50-70%                                             -1.815e+01  1.307e-08  3.044e+03 -0.006  0.99524   
stenose70-90%                                             -1.130e+00  3.229e-01  1.061e+00 -1.066  0.28657   
stenose90-99%                                             -1.243e+00  2.886e-01  1.065e+00 -1.167  0.24317   
stenose100% (Occlusion)                                   -1.839e+01  1.031e-08  9.618e+03 -0.002  0.99847   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                             -1.816e+01  1.293e-08  1.466e+04 -0.001  0.99901   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
hsCRP_plasma                                               1.645e-04  1.000e+00  9.860e-04  0.167  0.86752   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.156e+00  8.648e-01   0.64415    2.0758
Age                                                       9.937e-01  1.006e+00   0.95577    1.0331
Gendermale                                                2.163e+00  4.624e-01   1.02186    4.5765
Hypertension.compositeno                                  3.895e-01  2.567e+00   0.09125    1.6626
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.103e+00  9.064e-01   0.56119    2.1690
SmokerCurrentno                                           5.326e-01  1.878e+00   0.29080    0.9753
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          9.559e-01  1.046e+00   0.44820    2.0385
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.030e+00  9.708e-01   0.39499    2.6865
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.856e-01  1.015e+00   0.97006    1.0015
BMI                                                       9.806e-01  1.020e+00   0.90195    1.0661
CAD_history                                               2.755e+00  3.630e-01   1.47801    5.1341
Stroke_history                                            1.040e+00  9.612e-01   0.54707    1.9783
Peripheral.interv                                         1.431e+00  6.986e-01   0.75500    2.7140
stenose0-49%                                              1.243e-08  8.046e+07   0.00000       Inf
stenose50-70%                                             1.307e-08  7.652e+07   0.00000       Inf
stenose70-90%                                             3.229e-01  3.097e+00   0.04037    2.5823
stenose90-99%                                             2.886e-01  3.465e+00   0.03579    2.3264
stenose100% (Occlusion)                                   1.031e-08  9.704e+07   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             1.293e-08  7.734e+07   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.000e+00  9.998e-01   0.99823    1.0021

Concordance= 0.748  (se = 0.035 )
Likelihood ratio test= 38.67  on 20 df,   p=0.007
Wald test            = 24.85  on 20 df,   p=0.2
Score (logrank) test = 36.62  on 20 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.145257 
Standard error............: 0.298514 
Odds ratio (effect size)..: 1.156 
Lower 95% CI..............: 0.644 
Upper 95% CI..............: 2.076 
T-value...................: 0.486599 
P-value...................: 0.6265423 
Sample size in model......: 642 
Number of events..........: 48 
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 268, number of events= 16 
   (2120 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -5.623e-01  5.699e-01  5.176e-01 -1.086  0.27738    
Age                                                        9.014e-02  1.094e+00  3.751e-02  2.403  0.01626 *  
Gendermale                                                 5.128e-01  1.670e+00  6.414e-01  0.800  0.42399    
Hypertension.compositeno                                  -1.724e+01  3.243e-08  3.798e+03 -0.005  0.99638    
Hypertension.compositeyes                                  0.000e+00  1.000e+00  3.798e+03  0.000  1.00000    
DiabetesStatusDiabetes                                     1.099e+00  3.002e+00  5.398e-01  2.036  0.04174 *  
SmokerCurrentno                                           -8.228e-01  4.392e-01  5.015e-01 -1.641  0.10088    
SmokerCurrentyes                                           0.000e+00  1.000e+00  5.015e-01  0.000  1.00000    
Med.Statin.LLDno                                           5.213e-01  1.684e+00  5.187e-01  1.005  0.31487    
Med.Statin.LLDyes                                          0.000e+00  1.000e+00  5.187e-01  0.000  1.00000    
Med.all.antiplateletno                                    -2.972e-01  7.429e-01  1.033e+00 -0.288  0.77362    
Med.all.antiplateletyes                                    0.000e+00  1.000e+00  1.033e+00  0.000  1.00000    
GFR_MDRD                                                   3.542e-03  1.004e+00  1.385e-02  0.256  0.79815    
BMI                                                        2.457e-02  1.025e+00  6.104e-02  0.403  0.68731    
CAD_history                                                8.404e-01  2.317e+00  5.006e-01  1.679  0.09319 .  
Stroke_history                                             8.332e-01  2.301e+00  5.050e-01  1.650  0.09897 .  
Peripheral.interv                                          2.525e-01  1.287e+00  6.407e-01  0.394  0.69348    
stenose0-49%                                              -1.964e+00  1.403e-01  2.902e+04  0.000  0.99995    
stenose50-70%                                              1.189e+00  3.285e+00  9.652e+03  0.000  0.99990    
stenose70-90%                                              1.611e+01  9.915e+06  5.175e-01 31.127  < 2e-16 ***
stenose90-99%                                              1.550e+01  5.394e+06  5.175e-01 29.951  < 2e-16 ***
stenose100% (Occlusion)                                    0.000e+00  1.000e+00  1.187e+04  0.000  1.00000    
stenoseNA                                                  0.000e+00  1.000e+00  0.000e+00     NA       NA    
stenose50-99%                                              0.000e+00  1.000e+00  0.000e+00     NA       NA    
stenose70-99%                                              0.000e+00  1.000e+00  0.000e+00     NA       NA    
stenose99                                                  0.000e+00  1.000e+00  0.000e+00     NA       NA    
hsCRP_plasma                                               2.424e-03  1.002e+00  7.772e-04  3.119  0.00182 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 5.699e-01  1.755e+00 2.066e-01 1.572e+00
Age                                                       1.094e+00  9.138e-01 1.017e+00 1.178e+00
Gendermale                                                1.670e+00  5.988e-01 4.751e-01 5.870e+00
Hypertension.compositeno                                  3.243e-08  3.084e+07 0.000e+00       Inf
Hypertension.compositeyes                                 1.000e+00  1.000e+00 0.000e+00       Inf
DiabetesStatusDiabetes                                    3.002e+00  3.331e-01 1.042e+00 8.648e+00
SmokerCurrentno                                           4.392e-01  2.277e+00 1.643e-01 1.174e+00
SmokerCurrentyes                                          1.000e+00  1.000e+00 3.742e-01 2.672e+00
Med.Statin.LLDno                                          1.684e+00  5.937e-01 6.094e-01 4.655e+00
Med.Statin.LLDyes                                         1.000e+00  1.000e+00 3.618e-01 2.764e+00
Med.all.antiplateletno                                    7.429e-01  1.346e+00 9.804e-02 5.629e+00
Med.all.antiplateletyes                                   1.000e+00  1.000e+00 1.320e-01 7.577e+00
GFR_MDRD                                                  1.004e+00  9.965e-01 9.767e-01 1.031e+00
BMI                                                       1.025e+00  9.757e-01 9.093e-01 1.155e+00
CAD_history                                               2.317e+00  4.315e-01 8.687e-01 6.182e+00
Stroke_history                                            2.301e+00  4.346e-01 8.550e-01 6.191e+00
Peripheral.interv                                         1.287e+00  7.768e-01 3.667e-01 4.519e+00
stenose0-49%                                              1.403e-01  7.126e+00 0.000e+00       Inf
stenose50-70%                                             3.285e+00  3.044e-01 0.000e+00       Inf
stenose70-90%                                             9.915e+06  1.009e-07 3.596e+06 2.734e+07
stenose90-99%                                             5.394e+06  1.854e-07 1.956e+06 1.487e+07
stenose100% (Occlusion)                                   1.000e+00  1.000e+00 0.000e+00       Inf
stenoseNA                                                 1.000e+00  1.000e+00 1.000e+00 1.000e+00
stenose50-99%                                             1.000e+00  1.000e+00 1.000e+00 1.000e+00
stenose70-99%                                             1.000e+00  1.000e+00 1.000e+00 1.000e+00
stenose99                                                 1.000e+00  1.000e+00 1.000e+00 1.000e+00
hsCRP_plasma                                              1.002e+00  9.976e-01 1.001e+00 1.004e+00

Concordance= 0.832  (se = 0.048 )
Likelihood ratio test= 28.19  on 27 df,   p=0.4
Wald test            = 1882  on 27 df,   p=<2e-16
Score (logrank) test = 46.33  on 27 df,   p=0.01


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: IL6_rank 
Effect size...............: -0.562254 
Standard error............: 0.517624 
Odds ratio (effect size)..: 0.57 
Lower 95% CI..............: 0.207 
Upper 95% CI..............: 1.572 
T-value...................: -1.08622 
P-value...................: 0.2773817 
Sample size in model......: 268 
Number of events..........: 16 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 282, number of events= 17 
   (2106 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -3.478e-01  7.062e-01  4.862e-01 -0.715  0.47442    
Age                                                        9.615e-02  1.101e+00  3.755e-02  2.561  0.01044 *  
Gendermale                                                 3.885e-01  1.475e+00  6.366e-01  0.610  0.54174    
Hypertension.compositeno                                  -1.720e+01  3.376e-08  3.583e+03 -0.005  0.99617    
Hypertension.compositeyes                                  0.000e+00  1.000e+00  3.583e+03  0.000  1.00000    
DiabetesStatusDiabetes                                     9.760e-01  2.654e+00  5.326e-01  1.832  0.06689 .  
SmokerCurrentno                                           -9.194e-01  3.988e-01  4.867e-01 -1.889  0.05889 .  
SmokerCurrentyes                                           0.000e+00  1.000e+00  4.867e-01  0.000  1.00000    
Med.Statin.LLDno                                           4.881e-01  1.629e+00  5.090e-01  0.959  0.33763    
Med.Statin.LLDyes                                          0.000e+00  1.000e+00  5.090e-01  0.000  1.00000    
Med.all.antiplateletno                                    -7.568e-01  4.691e-01  1.031e+00 -0.734  0.46295    
Med.all.antiplateletyes                                    0.000e+00  1.000e+00  1.031e+00  0.000  1.00000    
GFR_MDRD                                                  -1.092e-02  9.891e-01  1.325e-02 -0.824  0.40988    
BMI                                                        6.871e-04  1.001e+00  5.983e-02  0.011  0.99084    
CAD_history                                                4.382e-01  1.550e+00  4.932e-01  0.888  0.37430    
Stroke_history                                             7.243e-01  2.063e+00  4.866e-01  1.488  0.13665    
Peripheral.interv                                          5.948e-01  1.813e+00  5.720e-01  1.040  0.29844    
stenose0-49%                                              -2.305e+00  9.975e-02  2.768e+04  0.000  0.99993    
stenose50-70%                                              1.434e+00  4.195e+00  9.963e+03  0.000  0.99989    
stenose70-90%                                              1.629e+01  1.184e+07  4.935e-01 33.003  < 2e-16 ***
stenose90-99%                                              1.545e+01  5.152e+06  4.935e-01 31.317  < 2e-16 ***
stenose100% (Occlusion)                                    0.000e+00  1.000e+00  1.157e+04  0.000  1.00000    
stenoseNA                                                  0.000e+00  1.000e+00  0.000e+00     NA       NA    
stenose50-99%                                              0.000e+00  1.000e+00  0.000e+00     NA       NA    
stenose70-99%                                              0.000e+00  1.000e+00  0.000e+00     NA       NA    
stenose99                                                  0.000e+00  1.000e+00  0.000e+00     NA       NA    
hsCRP_plasma                                               2.536e-03  1.003e+00  7.812e-04  3.246  0.00117 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 7.062e-01  1.416e+00 2.723e-01 1.832e+00
Age                                                       1.101e+00  9.083e-01 1.023e+00 1.185e+00
Gendermale                                                1.475e+00  6.781e-01 4.235e-01 5.136e+00
Hypertension.compositeno                                  3.376e-08  2.962e+07 0.000e+00       Inf
Hypertension.compositeyes                                 1.000e+00  1.000e+00 0.000e+00       Inf
DiabetesStatusDiabetes                                    2.654e+00  3.768e-01 9.343e-01 7.538e+00
SmokerCurrentno                                           3.988e-01  2.508e+00 1.536e-01 1.035e+00
SmokerCurrentyes                                          1.000e+00  1.000e+00 3.852e-01 2.596e+00
Med.Statin.LLDno                                          1.629e+00  6.138e-01 6.007e-01 4.418e+00
Med.Statin.LLDyes                                         1.000e+00  1.000e+00 3.687e-01 2.712e+00
Med.all.antiplateletno                                    4.691e-01  2.132e+00 6.217e-02 3.540e+00
Med.all.antiplateletyes                                   1.000e+00  1.000e+00 1.325e-01 7.546e+00
GFR_MDRD                                                  9.891e-01  1.011e+00 9.638e-01 1.015e+00
BMI                                                       1.001e+00  9.993e-01 8.900e-01 1.125e+00
CAD_history                                               1.550e+00  6.452e-01 5.895e-01 4.075e+00
Stroke_history                                            2.063e+00  4.847e-01 7.949e-01 5.355e+00
Peripheral.interv                                         1.813e+00  5.517e-01 5.908e-01 5.561e+00
stenose0-49%                                              9.975e-02  1.003e+01 0.000e+00       Inf
stenose50-70%                                             4.195e+00  2.384e-01 0.000e+00       Inf
stenose70-90%                                             1.184e+07  8.449e-08 4.499e+06 3.114e+07
stenose90-99%                                             5.152e+06  1.941e-07 1.958e+06 1.355e+07
stenose100% (Occlusion)                                   1.000e+00  1.000e+00 0.000e+00       Inf
stenoseNA                                                 1.000e+00  1.000e+00 1.000e+00 1.000e+00
stenose50-99%                                             1.000e+00  1.000e+00 1.000e+00 1.000e+00
stenose70-99%                                             1.000e+00  1.000e+00 1.000e+00 1.000e+00
stenose99                                                 1.000e+00  1.000e+00 1.000e+00 1.000e+00
hsCRP_plasma                                              1.003e+00  9.975e-01 1.001e+00 1.004e+00

Concordance= 0.828  (se = 0.045 )
Likelihood ratio test= 29.01  on 27 df,   p=0.4
Wald test            = 2090  on 27 df,   p=<2e-16
Score (logrank) test = 44.48  on 27 df,   p=0.02


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.347814 
Standard error............: 0.486247 
Odds ratio (effect size)..: 0.706 
Lower 95% CI..............: 0.272 
Upper 95% CI..............: 1.832 
T-value...................: -0.715304 
P-value...................: 0.4744211 
Sample size in model......: 282 
Number of events..........: 17 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 618, number of events= 25 
   (1770 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061]  5.868e-01  1.798e+00  4.349e-01  1.349  0.17728   
Age                                                        8.576e-02  1.090e+00  3.129e-02  2.741  0.00613 **
Gendermale                                                 1.461e+00  4.311e+00  6.332e-01  2.308  0.02102 * 
Hypertension.compositeno                                  -1.851e+01  9.172e-09  6.011e+03 -0.003  0.99754   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     3.247e-01  1.384e+00  4.744e-01  0.685  0.49364   
SmokerCurrentno                                           -7.793e-01  4.587e-01  4.542e-01 -1.716  0.08620 . 
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                          -1.421e-01  8.676e-01  5.120e-01 -0.277  0.78143   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     1.082e+00  2.950e+00  5.042e-01  2.145  0.03192 * 
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -2.560e-02  9.747e-01  1.086e-02 -2.358  0.01836 * 
BMI                                                        5.713e-02  1.059e+00  6.046e-02  0.945  0.34476   
CAD_history                                                3.951e-01  1.485e+00  4.308e-01  0.917  0.35911   
Stroke_history                                            -2.076e-01  8.126e-01  4.592e-01 -0.452  0.65126   
Peripheral.interv                                          6.257e-01  1.870e+00  5.055e-01  1.238  0.21575   
stenose0-49%                                              -2.006e+01  1.936e-09  3.815e+04 -0.001  0.99958   
stenose50-70%                                             -2.110e+00  1.213e-01  1.507e+00 -1.400  0.16148   
stenose70-90%                                             -1.796e+00  1.660e-01  1.153e+00 -1.558  0.11929   
stenose90-99%                                             -1.789e+00  1.671e-01  1.147e+00 -1.561  0.11862   
stenose100% (Occlusion)                                   -1.950e+01  3.397e-09  3.254e+04 -0.001  0.99952   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
hsCRP_plasma                                               1.842e-03  1.002e+00  8.093e-04  2.276  0.02284 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.798e+00  5.561e-01  0.766703    4.2175
Age                                                       1.090e+00  9.178e-01  1.024736    1.1584
Gendermale                                                4.311e+00  2.319e-01  1.246294   14.9145
Hypertension.compositeno                                  9.172e-09  1.090e+08  0.000000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.384e+00  7.227e-01  0.546052    3.5061
SmokerCurrentno                                           4.587e-01  2.180e+00  0.188334    1.1173
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          8.676e-01  1.153e+00  0.318014    2.3667
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    2.950e+00  3.390e-01  1.098017    7.9250
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.747e-01  1.026e+00  0.954200    0.9957
BMI                                                       1.059e+00  9.445e-01  0.940466    1.1920
CAD_history                                               1.485e+00  6.736e-01  0.638076    3.4537
Stroke_history                                            8.126e-01  1.231e+00  0.330342    1.9987
Peripheral.interv                                         1.870e+00  5.349e-01  0.694219    5.0349
stenose0-49%                                              1.936e-09  5.166e+08  0.000000       Inf
stenose50-70%                                             1.213e-01  8.246e+00  0.006326    2.3247
stenose70-90%                                             1.660e-01  6.023e+00  0.017338    1.5898
stenose90-99%                                             1.671e-01  5.986e+00  0.017657    1.5808
stenose100% (Occlusion)                                   3.397e-09  2.944e+08  0.000000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.002e+00  9.982e-01  1.000256    1.0034

Concordance= 0.845  (se = 0.032 )
Likelihood ratio test= 49.5  on 19 df,   p=2e-04
Wald test            = 18.65  on 19 df,   p=0.5
Score (logrank) test = 48.91  on 19 df,   p=2e-04


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: 0.586793 
Standard error............: 0.434931 
Odds ratio (effect size)..: 1.798 
Lower 95% CI..............: 0.767 
Upper 95% CI..............: 4.218 
T-value...................: 1.349163 
P-value...................: 0.1772846 
Sample size in model......: 618 
Number of events..........: 25 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 623, number of events= 25 
   (1765 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]  9.200e-01  2.509e+00  4.915e-01  1.872  0.06121 . 
Age                                                        8.762e-02  1.092e+00  3.174e-02  2.761  0.00577 **
Gendermale                                                 1.325e+00  3.763e+00  6.336e-01  2.092  0.03647 * 
Hypertension.compositeno                                  -1.845e+01  9.731e-09  6.057e+03 -0.003  0.99757   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     4.263e-01  1.532e+00  4.754e-01  0.897  0.36993   
SmokerCurrentno                                           -6.118e-01  5.424e-01  4.623e-01 -1.323  0.18576   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                          -2.450e-01  7.827e-01  5.154e-01 -0.475  0.63449   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     9.711e-01  2.641e+00  5.053e-01  1.922  0.05464 . 
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -2.516e-02  9.752e-01  1.078e-02 -2.333  0.01963 * 
BMI                                                        5.238e-02  1.054e+00  6.174e-02  0.848  0.39623   
CAD_history                                                4.604e-01  1.585e+00  4.289e-01  1.073  0.28310   
Stroke_history                                            -2.589e-01  7.719e-01  4.621e-01 -0.560  0.57534   
Peripheral.interv                                          4.685e-01  1.598e+00  5.021e-01  0.933  0.35079   
stenose0-49%                                              -1.988e+01  2.330e-09  4.926e+04  0.000  0.99968   
stenose50-70%                                             -2.693e+00  6.767e-02  1.568e+00 -1.717  0.08590 . 
stenose70-90%                                             -2.176e+00  1.135e-01  1.157e+00 -1.881  0.05997 . 
stenose90-99%                                             -2.298e+00  1.005e-01  1.168e+00 -1.967  0.04921 * 
stenose100% (Occlusion)                                   -1.992e+01  2.244e-09  3.186e+04 -0.001  0.99950   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                             -1.974e+01  2.667e-09  4.794e+04  0.000  0.99967   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
hsCRP_plasma                                               1.959e-03  1.002e+00  8.471e-04  2.313  0.02073 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 2.509e+00  3.985e-01  0.957679    6.5752
Age                                                       1.092e+00  9.161e-01  1.025738    1.1616
Gendermale                                                3.763e+00  2.657e-01  1.087054   13.0288
Hypertension.compositeno                                  9.731e-09  1.028e+08  0.000000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.532e+00  6.530e-01  0.603190    3.8885
SmokerCurrentno                                           5.424e-01  1.844e+00  0.219173    1.3423
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          7.827e-01  1.278e+00  0.285005    2.1494
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    2.641e+00  3.787e-01  0.980860    7.1100
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.752e-01  1.025e+00  0.954767    0.9960
BMI                                                       1.054e+00  9.490e-01  0.933673    1.1893
CAD_history                                               1.585e+00  6.310e-01  0.683667    3.6734
Stroke_history                                            7.719e-01  1.295e+00  0.312061    1.9095
Peripheral.interv                                         1.598e+00  6.259e-01  0.597114    4.2747
stenose0-49%                                              2.330e-09  4.293e+08  0.000000       Inf
stenose50-70%                                             6.767e-02  1.478e+01  0.003131    1.4628
stenose70-90%                                             1.135e-01  8.807e+00  0.011768    1.0956
stenose90-99%                                             1.005e-01  9.950e+00  0.010182    0.9921
stenose100% (Occlusion)                                   2.244e-09  4.457e+08  0.000000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.667e-09  3.749e+08  0.000000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.002e+00  9.980e-01  1.000299    1.0036

Concordance= 0.847  (se = 0.036 )
Likelihood ratio test= 51.36  on 20 df,   p=1e-04
Wald test            = 19.44  on 20 df,   p=0.5
Score (logrank) test = 52.12  on 20 df,   p=1e-04


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.920029 
Standard error............: 0.491474 
Odds ratio (effect size)..: 2.509 
Lower 95% CI..............: 0.958 
Upper 95% CI..............: 6.575 
T-value...................: 1.871978 
P-value...................: 0.06120959 
Sample size in model......: 623 
Number of events..........: 25 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + hsCRP_plasma, data = TEMP.DF)

  n= 642, number of events= 25 
   (1746 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] -1.961e-01  8.219e-01  4.254e-01 -0.461  0.64476   
Age                                                        8.139e-02  1.085e+00  3.150e-02  2.584  0.00977 **
Gendermale                                                 1.496e+00  4.463e+00  6.350e-01  2.355  0.01850 * 
Hypertension.compositeno                                  -1.747e+01  2.583e-08  3.750e+03 -0.005  0.99628   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     3.497e-01  1.419e+00  4.764e-01  0.734  0.46292   
SmokerCurrentno                                           -7.776e-01  4.595e-01  4.567e-01 -1.703  0.08859 . 
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                          -3.699e-02  9.637e-01  5.069e-01 -0.073  0.94182   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     1.008e+00  2.740e+00  5.031e-01  2.003  0.04513 * 
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -2.859e-02  9.718e-01  1.106e-02 -2.586  0.00972 **
BMI                                                        5.917e-02  1.061e+00  5.925e-02  0.999  0.31795   
CAD_history                                                4.010e-01  1.493e+00  4.292e-01  0.934  0.35016   
Stroke_history                                            -1.402e-01  8.692e-01  4.647e-01 -0.302  0.76288   
Peripheral.interv                                          5.585e-01  1.748e+00  5.050e-01  1.106  0.26872   
stenose0-49%                                              -1.954e+01  3.273e-09  2.318e+04 -0.001  0.99933   
stenose50-70%                                             -2.121e+00  1.199e-01  1.503e+00 -1.411  0.15838   
stenose70-90%                                             -1.974e+00  1.390e-01  1.154e+00 -1.710  0.08718 . 
stenose90-99%                                             -1.993e+00  1.362e-01  1.160e+00 -1.718  0.08581 . 
stenose100% (Occlusion)                                   -1.891e+01  6.112e-09  2.011e+04 -0.001  0.99925   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                             -1.890e+01  6.179e-09  3.012e+04 -0.001  0.99950   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
hsCRP_plasma                                               1.954e-03  1.002e+00  8.209e-04  2.380  0.01730 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 8.219e-01  1.217e+00  0.357032    1.8920
Age                                                       1.085e+00  9.218e-01  1.019845    1.1539
Gendermale                                                4.463e+00  2.241e-01  1.285442   15.4930
Hypertension.compositeno                                  2.583e-08  3.871e+07  0.000000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.419e+00  7.049e-01  0.557642    3.6091
SmokerCurrentno                                           4.595e-01  2.176e+00  0.187748    1.1246
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          9.637e-01  1.038e+00  0.356816    2.6027
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    2.740e+00  3.650e-01  1.022133    7.3447
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.718e-01  1.029e+00  0.950986    0.9931
BMI                                                       1.061e+00  9.425e-01  0.944634    1.1916
CAD_history                                               1.493e+00  6.697e-01  0.643894    3.4632
Stroke_history                                            8.692e-01  1.150e+00  0.349605    2.1610
Peripheral.interv                                         1.748e+00  5.721e-01  0.649724    4.7030
stenose0-49%                                              3.273e-09  3.055e+08  0.000000       Inf
stenose50-70%                                             1.199e-01  8.337e+00  0.006298    2.2843
stenose70-90%                                             1.390e-01  7.196e+00  0.014479    1.3336
stenose90-99%                                             1.362e-01  7.341e+00  0.014014    1.3242
stenose100% (Occlusion)                                   6.112e-09  1.636e+08  0.000000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             6.179e-09  1.618e+08  0.000000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
hsCRP_plasma                                              1.002e+00  9.980e-01  1.000345    1.0036

Concordance= 0.833  (se = 0.033 )
Likelihood ratio test= 48.16  on 20 df,   p=4e-04
Wald test            = 16.04  on 20 df,   p=0.7
Score (logrank) test = 47.77  on 20 df,   p=5e-04


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: -0.196136 
Standard error............: 0.425413 
Odds ratio (effect size)..: 0.822 
Lower 95% CI..............: 0.357 
Upper 95% CI..............: 1.892 
T-value...................: -0.461049 
P-value...................: 0.6447633 
Sample size in model......: 642 
Number of events..........: 25 

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL3.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)
object 'head.style' not found

MODEL 4

# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 4 same to model 2 with additional adjustment for IL6 levels in the plaque
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL4.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}
* Analyzing the effect of plaque proteins on [epmajor.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 368, number of events= 50 
   (2020 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -9.203e-02  9.121e-01  3.008e-01 -0.306  0.75961   
Age                                                        6.216e-02  1.064e+00  2.126e-02  2.924  0.00345 **
Gendermale                                                 1.064e+00  2.897e+00  4.251e-01  2.503  0.01233 * 
Hypertension.compositeno                                  -7.227e-01  4.854e-01  6.297e-01 -1.148  0.25112   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     5.258e-01  1.692e+00  3.356e-01  1.567  0.11714   
SmokerCurrentno                                           -5.752e-01  5.626e-01  3.027e-01 -1.900  0.05740 . 
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                           3.211e-01  1.379e+00  3.247e-01  0.989  0.32266   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     1.783e-01  1.195e+00  4.485e-01  0.398  0.69099   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -9.516e-03  9.905e-01  7.737e-03 -1.230  0.21874   
BMI                                                        9.862e-03  1.010e+00  4.458e-02  0.221  0.82492   
CAD_history                                                5.590e-01  1.749e+00  3.084e-01  1.813  0.06985 . 
Stroke_history                                             2.059e-02  1.021e+00  3.090e-01  0.067  0.94687   
Peripheral.interv                                          1.198e-01  1.127e+00  3.666e-01  0.327  0.74372   
stenose0-49%                                              -1.676e+01  5.238e-08  3.616e+03 -0.005  0.99630   
stenose50-70%                                             -1.716e+00  1.798e-01  1.537e+00 -1.116  0.26426   
stenose70-90%                                             -1.110e+00  3.294e-01  1.124e+00 -0.987  0.32343   
stenose90-99%                                             -1.331e+00  2.643e-01  1.141e+00 -1.166  0.24357   
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
IL6_pg_ug_2015_rank                                        1.331e-01  1.142e+00  1.516e-01  0.878  0.37985   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 9.121e-01  1.096e+00  0.505860     1.645
Age                                                       1.064e+00  9.397e-01  1.020708     1.109
Gendermale                                                2.897e+00  3.451e-01  1.259434     6.665
Hypertension.compositeno                                  4.854e-01  2.060e+00  0.141289     1.668
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.692e+00  5.911e-01  0.876431     3.266
SmokerCurrentno                                           5.626e-01  1.777e+00  0.310871     1.018
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.379e+00  7.253e-01  0.729599     2.605
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.195e+00  8.367e-01  0.496232     2.878
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.905e-01  1.010e+00  0.975621     1.006
BMI                                                       1.010e+00  9.902e-01  0.925414     1.102
CAD_history                                               1.749e+00  5.718e-01  0.955666     3.201
Stroke_history                                            1.021e+00  9.796e-01  0.557041     1.871
Peripheral.interv                                         1.127e+00  8.871e-01  0.549588     2.312
stenose0-49%                                              5.238e-08  1.909e+07  0.000000       Inf
stenose50-70%                                             1.798e-01  5.563e+00  0.008836     3.657
stenose70-90%                                             3.294e-01  3.035e+00  0.036359     2.985
stenose90-99%                                             2.643e-01  3.784e+00  0.028233     2.474
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank                                       1.142e+00  8.754e-01  0.848765     1.538

Concordance= 0.74  (se = 0.034 )
Likelihood ratio test= 37.52  on 18 df,   p=0.004
Wald test            = 33.01  on 18 df,   p=0.02
Score (logrank) test = 35.91  on 18 df,   p=0.007


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: IL6_rank 
Effect size...............: -0.092028 
Standard error............: 0.300754 
Odds ratio (effect size)..: 0.912 
Lower 95% CI..............: 0.506 
Upper 95% CI..............: 1.645 
T-value...................: -0.30599 
P-value...................: 0.759612 
Sample size in model......: 368 
Number of events..........: 50 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 392, number of events= 50 
   (1996 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -5.238e-01  5.923e-01  3.055e-01 -1.715  0.08639 . 
Age                                                        5.763e-02  1.059e+00  2.098e-02  2.747  0.00601 **
Gendermale                                                 1.092e+00  2.981e+00  4.240e-01  2.576  0.00999 **
Hypertension.compositeno                                  -7.803e-01  4.583e-01  6.261e-01 -1.246  0.21268   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     4.063e-01  1.501e+00  3.367e-01  1.207  0.22760   
SmokerCurrentno                                           -4.639e-01  6.288e-01  3.079e-01 -1.507  0.13181   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                           4.195e-01  1.521e+00  3.257e-01  1.288  0.19774   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     9.793e-02  1.103e+00  4.530e-01  0.216  0.82883   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -1.674e-02  9.834e-01  7.946e-03 -2.106  0.03519 * 
BMI                                                        2.065e-03  1.002e+00  4.407e-02  0.047  0.96263   
CAD_history                                                3.553e-01  1.427e+00  3.145e-01  1.130  0.25855   
Stroke_history                                             1.127e-01  1.119e+00  3.028e-01  0.372  0.70972   
Peripheral.interv                                          1.911e-01  1.211e+00  3.653e-01  0.523  0.60093   
stenose0-49%                                              -1.665e+01  5.854e-08  3.722e+03 -0.004  0.99643   
stenose50-70%                                             -1.447e+00  2.353e-01  1.534e+00 -0.943  0.34543   
stenose70-90%                                             -1.012e+00  3.634e-01  1.117e+00 -0.907  0.36460   
stenose90-99%                                             -1.217e+00  2.961e-01  1.139e+00 -1.068  0.28537   
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
IL6_pg_ug_2015_rank                                        2.158e-01  1.241e+00  1.539e-01  1.402  0.16080   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 5.923e-01  1.688e+00   0.32548    1.0778
Age                                                       1.059e+00  9.440e-01   1.01665    1.1038
Gendermale                                                2.981e+00  3.355e-01   1.29849    6.8430
Hypertension.compositeno                                  4.583e-01  2.182e+00   0.13432    1.5635
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.501e+00  6.661e-01   0.77594    2.9043
SmokerCurrentno                                           6.288e-01  1.590e+00   0.34393    1.1496
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.521e+00  6.574e-01   0.80343    2.8804
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.103e+00  9.067e-01   0.45391    2.6797
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.834e-01  1.017e+00   0.96821    0.9988
BMI                                                       1.002e+00  9.979e-01   0.91914    1.0925
CAD_history                                               1.427e+00  7.010e-01   0.77023    2.6424
Stroke_history                                            1.119e+00  8.934e-01   0.61834    2.0261
Peripheral.interv                                         1.211e+00  8.261e-01   0.59163    2.4769
stenose0-49%                                              5.854e-08  1.708e+07   0.00000       Inf
stenose50-70%                                             2.353e-01  4.250e+00   0.01165    4.7531
stenose70-90%                                             3.634e-01  2.752e+00   0.04072    3.2419
stenose90-99%                                             2.961e-01  3.377e+00   0.03175    2.7615
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank                                       1.241e+00  8.059e-01   0.91776    1.6778

Concordance= 0.753  (se = 0.033 )
Likelihood ratio test= 42.55  on 18 df,   p=9e-04
Wald test            = 37.59  on 18 df,   p=0.004
Score (logrank) test = 40.38  on 18 df,   p=0.002


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.523784 
Standard error............: 0.305456 
Odds ratio (effect size)..: 0.592 
Lower 95% CI..............: 0.325 
Upper 95% CI..............: 1.078 
T-value...................: -1.714758 
P-value...................: 0.08638969 
Sample size in model......: 392 
Number of events..........: 50 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 1002, number of events= 114 
   (1386 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061]  9.097e-02  1.095e+00  1.899e-01  0.479 0.631870    
Age                                                        3.856e-02  1.039e+00  1.296e-02  2.975 0.002927 ** 
Gendermale                                                 5.730e-01  1.774e+00  2.310e-01  2.481 0.013107 *  
Hypertension.compositeno                                  -5.174e-01  5.961e-01  3.773e-01 -1.371 0.170285    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -4.403e-02  9.569e-01  2.233e-01 -0.197 0.843644    
SmokerCurrentno                                           -6.023e-01  5.475e-01  2.046e-01 -2.944 0.003240 ** 
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           3.369e-01  1.401e+00  2.175e-01  1.549 0.121332    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     4.026e-01  1.496e+00  2.586e-01  1.557 0.119528    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -1.899e-02  9.812e-01  4.977e-03 -3.815 0.000136 ***
BMI                                                        5.747e-02  1.059e+00  2.597e-02  2.213 0.026905 *  
CAD_history                                                1.411e-01  1.152e+00  2.032e-01  0.695 0.487257    
Stroke_history                                             4.304e-02  1.044e+00  2.028e-01  0.212 0.831900    
Peripheral.interv                                          6.318e-01  1.881e+00  2.183e-01  2.894 0.003802 ** 
stenose0-49%                                              -1.561e+01  1.659e-07  2.745e+03 -0.006 0.995462    
stenose50-70%                                             -8.519e-01  4.266e-01  8.692e-01 -0.980 0.326998    
stenose70-90%                                             -2.953e-01  7.443e-01  7.264e-01 -0.406 0.684395    
stenose90-99%                                             -2.722e-01  7.617e-01  7.236e-01 -0.376 0.706782    
stenose100% (Occlusion)                                    4.662e-02  1.048e+00  1.239e+00  0.038 0.969996    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.539e+01  2.069e-07  4.208e+03 -0.004 0.997082    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.095e+00  9.130e-01   0.75490    1.5890
Age                                                       1.039e+00  9.622e-01   1.01325    1.0660
Gendermale                                                1.774e+00  5.638e-01   1.12785    2.7890
Hypertension.compositeno                                  5.961e-01  1.678e+00   0.28453    1.2487
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.569e-01  1.045e+00   0.61778    1.4822
SmokerCurrentno                                           5.475e-01  1.826e+00   0.36664    0.8176
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.401e+00  7.140e-01   0.91454    2.1451
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.496e+00  6.686e-01   0.90098    2.4831
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.812e-01  1.019e+00   0.97167    0.9908
BMI                                                       1.059e+00  9.442e-01   1.00659    1.1145
CAD_history                                               1.152e+00  8.684e-01   0.77330    1.7150
Stroke_history                                            1.044e+00  9.579e-01   0.70161    1.5534
Peripheral.interv                                         1.881e+00  5.316e-01   1.22621    2.8853
stenose0-49%                                              1.659e-07  6.027e+06   0.00000       Inf
stenose50-70%                                             4.266e-01  2.344e+00   0.07765    2.3434
stenose70-90%                                             7.443e-01  1.343e+00   0.17924    3.0909
stenose90-99%                                             7.617e-01  1.313e+00   0.18446    3.1454
stenose100% (Occlusion)                                   1.048e+00  9.545e-01   0.09231   11.8918
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.069e-07  4.833e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                            NA         NA        NA        NA

Concordance= 0.705  (se = 0.023 )
Likelihood ratio test= 68.36  on 19 df,   p=2e-07
Wald test            = 38.72  on 19 df,   p=0.005
Score (logrank) test = 66.86  on 19 df,   p=3e-07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: 0.090966 
Standard error............: 0.189871 
Odds ratio (effect size)..: 1.095 
Lower 95% CI..............: 0.755 
Upper 95% CI..............: 1.589 
T-value...................: 0.479096 
P-value...................: 0.6318701 
Sample size in model......: 1002 
Number of events..........: 114 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 970, number of events= 113 
   (1418 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]  3.455e-01  1.413e+00  2.077e-01  1.663 0.096227 .  
Age                                                        3.817e-02  1.039e+00  1.312e-02  2.908 0.003635 ** 
Gendermale                                                 5.637e-01  1.757e+00  2.340e-01  2.409 0.016010 *  
Hypertension.compositeno                                  -5.208e-01  5.941e-01  3.784e-01 -1.376 0.168720    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -5.564e-02  9.459e-01  2.253e-01 -0.247 0.804978    
SmokerCurrentno                                           -5.877e-01  5.556e-01  2.061e-01 -2.851 0.004359 ** 
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           2.948e-01  1.343e+00  2.206e-01  1.337 0.181368    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     4.108e-01  1.508e+00  2.591e-01  1.586 0.112845    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -1.747e-02  9.827e-01  5.018e-03 -3.481 0.000499 ***
BMI                                                        5.835e-02  1.060e+00  2.728e-02  2.139 0.032435 *  
CAD_history                                                1.816e-01  1.199e+00  2.041e-01  0.890 0.373610    
Stroke_history                                             3.289e-02  1.033e+00  2.028e-01  0.162 0.871165    
Peripheral.interv                                          6.233e-01  1.865e+00  2.186e-01  2.851 0.004362 ** 
stenose0-49%                                              -1.443e+01  5.407e-07  1.940e+03 -0.007 0.994066    
stenose50-70%                                             -8.746e-01  4.170e-01  8.691e-01 -1.006 0.314250    
stenose70-90%                                             -3.829e-01  6.819e-01  7.286e-01 -0.526 0.599224    
stenose90-99%                                             -3.973e-01  6.721e-01  7.273e-01 -0.546 0.584875    
stenose100% (Occlusion)                                   -5.011e-02  9.511e-01  1.239e+00 -0.040 0.967735    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.453e+01  4.871e-07  2.498e+03 -0.006 0.995358    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                     -1.395e-02  9.862e-01  1.990e-01 -0.070 0.944120    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.413e+00  7.079e-01   0.94028    2.1223
Age                                                       1.039e+00  9.626e-01   1.01252    1.0660
Gendermale                                                1.757e+00  5.691e-01   1.11073    2.7799
Hypertension.compositeno                                  5.941e-01  1.683e+00   0.28299    1.2471
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.459e-01  1.057e+00   0.60818    1.4711
SmokerCurrentno                                           5.556e-01  1.800e+00   0.37094    0.8322
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.343e+00  7.447e-01   0.87153    2.0691
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.508e+00  6.631e-01   0.90754    2.5059
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.827e-01  1.018e+00   0.97307    0.9924
BMI                                                       1.060e+00  9.433e-01   1.00490    1.1183
CAD_history                                               1.199e+00  8.339e-01   0.80377    1.7890
Stroke_history                                            1.033e+00  9.676e-01   0.69450    1.5378
Peripheral.interv                                         1.865e+00  5.362e-01   1.21501    2.8627
stenose0-49%                                              5.407e-07  1.850e+06   0.00000       Inf
stenose50-70%                                             4.170e-01  2.398e+00   0.07593    2.2905
stenose70-90%                                             6.819e-01  1.467e+00   0.16350    2.8438
stenose90-99%                                             6.721e-01  1.488e+00   0.16157    2.7960
stenose100% (Occlusion)                                   9.511e-01  1.051e+00   0.08388   10.7852
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             4.871e-07  2.053e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                     9.862e-01  1.014e+00   0.66771    1.4565

Concordance= 0.706  (se = 0.023 )
Likelihood ratio test= 68.71  on 20 df,   p=3e-07
Wald test            = 64.7  on 20 df,   p=1e-06
Score (logrank) test = 68.67  on 20 df,   p=3e-07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.345452 
Standard error............: 0.207675 
Odds ratio (effect size)..: 1.413 
Lower 95% CI..............: 0.94 
Upper 95% CI..............: 2.122 
T-value...................: 1.663429 
P-value...................: 0.09622664 
Sample size in model......: 970 
Number of events..........: 113 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 1001, number of events= 114 
   (1387 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148]  1.631e-01  1.177e+00  1.990e-01  0.820 0.412212    
Age                                                        3.772e-02  1.038e+00  1.299e-02  2.905 0.003673 ** 
Gendermale                                                 5.672e-01  1.763e+00  2.311e-01  2.454 0.014136 *  
Hypertension.compositeno                                  -5.363e-01  5.849e-01  3.781e-01 -1.419 0.156010    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -4.504e-02  9.560e-01  2.233e-01 -0.202 0.840147    
SmokerCurrentno                                           -6.012e-01  5.481e-01  2.049e-01 -2.934 0.003343 ** 
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           3.413e-01  1.407e+00  2.178e-01  1.567 0.117091    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     3.958e-01  1.486e+00  2.585e-01  1.531 0.125667    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -1.933e-02  9.809e-01  4.975e-03 -3.885 0.000102 ***
BMI                                                        5.855e-02  1.060e+00  2.624e-02  2.231 0.025676 *  
CAD_history                                                1.486e-01  1.160e+00  2.035e-01  0.730 0.465433    
Stroke_history                                             2.847e-02  1.029e+00  2.032e-01  0.140 0.888591    
Peripheral.interv                                          6.295e-01  1.877e+00  2.185e-01  2.881 0.003959 ** 
stenose0-49%                                              -1.552e+01  1.820e-07  2.775e+03 -0.006 0.995538    
stenose50-70%                                             -8.172e-01  4.417e-01  8.704e-01 -0.939 0.347787    
stenose70-90%                                             -2.432e-01  7.841e-01  7.292e-01 -0.333 0.738758    
stenose90-99%                                             -2.113e-01  8.095e-01  7.269e-01 -0.291 0.771249    
stenose100% (Occlusion)                                    1.271e-01  1.136e+00  1.243e+00  0.102 0.918513    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.545e+01  1.947e-07  4.214e+03 -0.004 0.997074    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                      4.243e-02  1.043e+00  1.977e-01  0.215 0.830048    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.177e+00  8.495e-01   0.79708    1.7386
Age                                                       1.038e+00  9.630e-01   1.01235    1.0652
Gendermale                                                1.763e+00  5.671e-01   1.12091    2.7737
Hypertension.compositeno                                  5.849e-01  1.710e+00   0.27877    1.2271
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.560e-01  1.046e+00   0.61712    1.4808
SmokerCurrentno                                           5.481e-01  1.824e+00   0.36684    0.8190
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.407e+00  7.109e-01   0.91800    2.1557
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.486e+00  6.731e-01   0.89515    2.4654
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.809e-01  1.020e+00   0.97134    0.9905
BMI                                                       1.060e+00  9.431e-01   1.00714    1.1163
CAD_history                                               1.160e+00  8.620e-01   0.77854    1.7288
Stroke_history                                            1.029e+00  9.719e-01   0.69081    1.5324
Peripheral.interv                                         1.877e+00  5.329e-01   1.22298    2.8795
stenose0-49%                                              1.820e-07  5.496e+06   0.00000       Inf
stenose50-70%                                             4.417e-01  2.264e+00   0.08022    2.4320
stenose70-90%                                             7.841e-01  1.275e+00   0.18778    3.2742
stenose90-99%                                             8.095e-01  1.235e+00   0.19476    3.3647
stenose100% (Occlusion)                                   1.136e+00  8.806e-01   0.09940   12.9735
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             1.947e-07  5.136e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                     1.043e+00  9.585e-01   0.70818    1.5371

Concordance= 0.707  (se = 0.023 )
Likelihood ratio test= 69.11  on 20 df,   p=3e-07
Wald test            = 39.27  on 20 df,   p=0.006
Score (logrank) test = 67.72  on 20 df,   p=4e-07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epmajor.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.163144 
Standard error............: 0.198954 
Odds ratio (effect size)..: 1.177 
Lower 95% CI..............: 0.797 
Upper 95% CI..............: 1.739 
T-value...................: 0.820007 
P-value...................: 0.4122119 
Sample size in model......: 1001 
Number of events..........: 114 
* Analyzing the effect of plaque proteins on [epstroke.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 368, number of events= 26 
   (2020 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -1.558e-01  8.557e-01  4.112e-01 -0.379    0.705  
Age                                                        4.907e-02  1.050e+00  2.821e-02  1.739    0.082 .
Gendermale                                                 6.220e-01  1.863e+00  5.170e-01  1.203    0.229  
Hypertension.compositeno                                  -4.395e-01  6.444e-01  7.733e-01 -0.568    0.570  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                     2.188e-01  1.245e+00  4.921e-01  0.445    0.657  
SmokerCurrentno                                           -3.138e-01  7.307e-01  4.315e-01 -0.727    0.467  
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA  
Med.Statin.LLDno                                           2.360e-01  1.266e+00  4.439e-01  0.532    0.595  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     3.981e-01  1.489e+00  6.140e-01  0.648    0.517  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -1.981e-03  9.980e-01  1.108e-02 -0.179    0.858  
BMI                                                        2.002e-02  1.020e+00  5.918e-02  0.338    0.735  
CAD_history                                                1.966e-01  1.217e+00  4.458e-01  0.441    0.659  
Stroke_history                                             3.242e-01  1.383e+00  4.081e-01  0.794    0.427  
Peripheral.interv                                         -5.563e-01  5.733e-01  6.377e-01 -0.872    0.383  
stenose0-49%                                              -1.864e+01  8.070e-09  1.496e+04 -0.001    0.999  
stenose50-70%                                             -1.832e+01  1.105e-08  5.344e+03 -0.003    0.997  
stenose70-90%                                             -1.118e+00  3.269e-01  1.200e+00 -0.932    0.352  
stenose90-99%                                             -1.281e+00  2.779e-01  1.218e+00 -1.051    0.293  
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
IL6_pg_ug_2015_rank                                        2.528e-02  1.026e+00  2.000e-01  0.126    0.899  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 8.557e-01  1.169e+00   0.38224     1.916
Age                                                       1.050e+00  9.521e-01   0.99379     1.110
Gendermale                                                1.863e+00  5.369e-01   0.67615     5.131
Hypertension.compositeno                                  6.444e-01  1.552e+00   0.14155     2.933
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.245e+00  8.035e-01   0.47437     3.265
SmokerCurrentno                                           7.307e-01  1.369e+00   0.31362     1.702
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.266e+00  7.897e-01   0.53048     3.022
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.489e+00  6.716e-01   0.44694     4.961
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.980e-01  1.002e+00   0.97658     1.020
BMI                                                       1.020e+00  9.802e-01   0.90848     1.146
CAD_history                                               1.217e+00  8.215e-01   0.50803     2.917
Stroke_history                                            1.383e+00  7.231e-01   0.62143     3.077
Peripheral.interv                                         5.733e-01  1.744e+00   0.16427     2.001
stenose0-49%                                              8.070e-09  1.239e+08   0.00000       Inf
stenose50-70%                                             1.105e-08  9.053e+07   0.00000       Inf
stenose70-90%                                             3.269e-01  3.059e+00   0.03111     3.436
stenose90-99%                                             2.779e-01  3.599e+00   0.02551     3.026
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank                                       1.026e+00  9.750e-01   0.69298     1.518

Concordance= 0.697  (se = 0.05 )
Likelihood ratio test= 13.52  on 18 df,   p=0.8
Wald test            = 10.22  on 18 df,   p=0.9
Score (logrank) test = 12.74  on 18 df,   p=0.8


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: IL6_rank 
Effect size...............: -0.1558 
Standard error............: 0.411186 
Odds ratio (effect size)..: 0.856 
Lower 95% CI..............: 0.382 
Upper 95% CI..............: 1.916 
T-value...................: -0.378903 
P-value...................: 0.7047602 
Sample size in model......: 368 
Number of events..........: 26 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 392, number of events= 25 
   (1996 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -5.328e-01  5.870e-01  4.316e-01 -1.234   0.2171  
Age                                                        5.067e-02  1.052e+00  2.851e-02  1.777   0.0755 .
Gendermale                                                 6.017e-01  1.825e+00  5.221e-01  1.152   0.2492  
Hypertension.compositeno                                  -4.954e-01  6.093e-01  7.676e-01 -0.645   0.5186  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                    -6.285e-02  9.391e-01  5.253e-01 -0.120   0.9048  
SmokerCurrentno                                           -2.323e-01  7.927e-01  4.489e-01 -0.518   0.6047  
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA  
Med.Statin.LLDno                                           3.932e-01  1.482e+00  4.510e-01  0.872   0.3834  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     2.429e-01  1.275e+00  6.182e-01  0.393   0.6944  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -1.360e-03  9.986e-01  1.164e-02 -0.117   0.9069  
BMI                                                        1.719e-02  1.017e+00  5.977e-02  0.288   0.7737  
CAD_history                                                1.039e-01  1.110e+00  4.651e-01  0.223   0.8232  
Stroke_history                                             3.472e-01  1.415e+00  4.174e-01  0.832   0.4055  
Peripheral.interv                                         -4.019e-01  6.690e-01  6.389e-01 -0.629   0.5293  
stenose0-49%                                              -1.875e+01  7.162e-09  1.687e+04 -0.001   0.9991  
stenose50-70%                                             -1.832e+01  1.109e-08  5.743e+03 -0.003   0.9975  
stenose70-90%                                             -1.081e+00  3.392e-01  1.194e+00 -0.905   0.3653  
stenose90-99%                                             -1.274e+00  2.797e-01  1.221e+00 -1.044   0.2966  
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
IL6_pg_ug_2015_rank                                        8.011e-02  1.083e+00  2.080e-01  0.385   0.7001  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 5.870e-01  1.704e+00   0.25192     1.368
Age                                                       1.052e+00  9.506e-01   0.99481     1.112
Gendermale                                                1.825e+00  5.479e-01   0.65593     5.079
Hypertension.compositeno                                  6.093e-01  1.641e+00   0.13536     2.743
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.391e-01  1.065e+00   0.33541     2.629
SmokerCurrentno                                           7.927e-01  1.262e+00   0.32887     1.911
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.482e+00  6.749e-01   0.61212     3.586
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.275e+00  7.844e-01   0.37954     4.282
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.986e-01  1.001e+00   0.97612     1.022
BMI                                                       1.017e+00  9.830e-01   0.90488     1.144
CAD_history                                               1.110e+00  9.013e-01   0.44588     2.761
Stroke_history                                            1.415e+00  7.067e-01   0.62443     3.207
Peripheral.interv                                         6.690e-01  1.495e+00   0.19124     2.340
stenose0-49%                                              7.162e-09  1.396e+08   0.00000       Inf
stenose50-70%                                             1.109e-08  9.020e+07   0.00000       Inf
stenose70-90%                                             3.392e-01  2.949e+00   0.03264     3.524
stenose90-99%                                             2.797e-01  3.575e+00   0.02557     3.059
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank                                       1.083e+00  9.230e-01   0.72074     1.629

Concordance= 0.697  (se = 0.05 )
Likelihood ratio test= 14.65  on 18 df,   p=0.7
Wald test            = 12.01  on 18 df,   p=0.8
Score (logrank) test = 14.49  on 18 df,   p=0.7


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.532752 
Standard error............: 0.431585 
Odds ratio (effect size)..: 0.587 
Lower 95% CI..............: 0.252 
Upper 95% CI..............: 1.368 
T-value...................: -1.234407 
P-value...................: 0.2170514 
Sample size in model......: 392 
Number of events..........: 25 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 1002, number of events= 58 
   (1386 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] -1.438e-01  8.660e-01  2.656e-01 -0.541  0.58818   
Age                                                        5.026e-02  1.052e+00  1.793e-02  2.803  0.00506 **
Gendermale                                                 2.919e-01  1.339e+00  3.000e-01  0.973  0.33043   
Hypertension.compositeno                                  -2.080e-01  8.122e-01  4.442e-01 -0.468  0.63956   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                    -6.073e-02  9.411e-01  3.197e-01 -0.190  0.84935   
SmokerCurrentno                                           -3.427e-01  7.098e-01  2.939e-01 -1.166  0.24358   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                           3.978e-01  1.488e+00  2.912e-01  1.366  0.17192   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     3.468e-01  1.414e+00  3.748e-01  0.925  0.35485   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -5.639e-03  9.944e-01  7.004e-03 -0.805  0.42078   
BMI                                                        9.236e-02  1.097e+00  3.357e-02  2.751  0.00593 **
CAD_history                                               -5.750e-01  5.627e-01  3.288e-01 -1.749  0.08031 . 
Stroke_history                                             3.241e-01  1.383e+00  2.748e-01  1.179  0.23826   
Peripheral.interv                                          4.887e-01  1.630e+00  3.276e-01  1.492  0.13578   
stenose0-49%                                              -1.559e+01  1.699e-07  3.740e+03 -0.004  0.99667   
stenose50-70%                                             -5.898e-01  5.544e-01  1.159e+00 -0.509  0.61097   
stenose70-90%                                             -3.521e-01  7.032e-01  1.025e+00 -0.343  0.73127   
stenose90-99%                                             -3.578e-01  6.992e-01  1.024e+00 -0.349  0.72672   
stenose100% (Occlusion)                                    3.875e-01  1.473e+00  1.439e+00  0.269  0.78767   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                             -1.537e+01  2.109e-07  5.615e+03 -0.003  0.99782   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                             NA         NA  0.000e+00     NA       NA   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 8.660e-01  1.155e+00   0.51454     1.458
Age                                                       1.052e+00  9.510e-01   1.01523     1.089
Gendermale                                                1.339e+00  7.468e-01   0.74380     2.411
Hypertension.compositeno                                  8.122e-01  1.231e+00   0.34009     1.940
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.411e-01  1.063e+00   0.50289     1.761
SmokerCurrentno                                           7.098e-01  1.409e+00   0.39898     1.263
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.488e+00  6.718e-01   0.84120     2.634
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.414e+00  7.070e-01   0.67854     2.949
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.944e-01  1.006e+00   0.98082     1.008
BMI                                                       1.097e+00  9.118e-01   1.02692     1.171
CAD_history                                               5.627e-01  1.777e+00   0.29542     1.072
Stroke_history                                            1.383e+00  7.232e-01   0.80691     2.370
Peripheral.interv                                         1.630e+00  6.134e-01   0.85779     3.098
stenose0-49%                                              1.699e-07  5.887e+06   0.00000       Inf
stenose50-70%                                             5.544e-01  1.804e+00   0.05713     5.380
stenose70-90%                                             7.032e-01  1.422e+00   0.09429     5.245
stenose90-99%                                             6.992e-01  1.430e+00   0.09401     5.200
stenose100% (Occlusion)                                   1.473e+00  6.788e-01   0.08784    24.711
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.109e-07  4.742e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                            NA         NA        NA        NA

Concordance= 0.682  (se = 0.036 )
Likelihood ratio test= 27.19  on 19 df,   p=0.1
Wald test            = 22.76  on 19 df,   p=0.2
Score (logrank) test = 26.28  on 19 df,   p=0.1


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: -0.143835 
Standard error............: 0.265636 
Odds ratio (effect size)..: 0.866 
Lower 95% CI..............: 0.515 
Upper 95% CI..............: 1.458 
T-value...................: -0.541475 
P-value...................: 0.5881802 
Sample size in model......: 1002 
Number of events..........: 58 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 970, number of events= 57 
   (1418 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]  1.922e-01  1.212e+00  2.849e-01  0.675  0.49989   
Age                                                        4.981e-02  1.051e+00  1.823e-02  2.733  0.00628 **
Gendermale                                                 2.945e-01  1.342e+00  3.063e-01  0.961  0.33642   
Hypertension.compositeno                                  -2.189e-01  8.034e-01  4.448e-01 -0.492  0.62257   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                    -1.150e-01  8.914e-01  3.264e-01 -0.352  0.72473   
SmokerCurrentno                                           -3.442e-01  7.088e-01  2.961e-01 -1.162  0.24510   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                           3.949e-01  1.484e+00  2.960e-01  1.334  0.18224   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     3.615e-01  1.436e+00  3.750e-01  0.964  0.33495   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -3.047e-03  9.970e-01  7.227e-03 -0.422  0.67331   
BMI                                                        9.901e-02  1.104e+00  3.632e-02  2.726  0.00640 **
CAD_history                                               -5.360e-01  5.851e-01  3.312e-01 -1.618  0.10561   
Stroke_history                                             3.457e-01  1.413e+00  2.759e-01  1.253  0.21027   
Peripheral.interv                                          5.297e-01  1.698e+00  3.294e-01  1.608  0.10779   
stenose0-49%                                              -1.417e+01  7.014e-07  2.855e+03 -0.005  0.99604   
stenose50-70%                                             -5.990e-01  5.494e-01  1.159e+00 -0.517  0.60543   
stenose70-90%                                             -3.751e-01  6.872e-01  1.026e+00 -0.366  0.71466   
stenose90-99%                                             -4.397e-01  6.443e-01  1.027e+00 -0.428  0.66855   
stenose100% (Occlusion)                                    3.483e-01  1.417e+00  1.436e+00  0.242  0.80840   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                             -1.446e+01  5.246e-07  3.454e+03 -0.004  0.99666   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                     -1.892e-01  8.277e-01  2.775e-01 -0.682  0.49539   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.212e+00  8.251e-01   0.69340     2.118
Age                                                       1.051e+00  9.514e-01   1.01418     1.089
Gendermale                                                1.342e+00  7.449e-01   0.73643     2.447
Hypertension.compositeno                                  8.034e-01  1.245e+00   0.33600     1.921
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    8.914e-01  1.122e+00   0.47012     1.690
SmokerCurrentno                                           7.088e-01  1.411e+00   0.39671     1.266
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.484e+00  6.738e-01   0.83083     2.651
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.436e+00  6.966e-01   0.68841     2.993
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.970e-01  1.003e+00   0.98294     1.011
BMI                                                       1.104e+00  9.057e-01   1.02822     1.186
CAD_history                                               5.851e-01  1.709e+00   0.30571     1.120
Stroke_history                                            1.413e+00  7.078e-01   0.82275     2.426
Peripheral.interv                                         1.698e+00  5.888e-01   0.89060     3.239
stenose0-49%                                              7.014e-07  1.426e+06   0.00000       Inf
stenose50-70%                                             5.494e-01  1.820e+00   0.05661     5.331
stenose70-90%                                             6.872e-01  1.455e+00   0.09199     5.134
stenose90-99%                                             6.443e-01  1.552e+00   0.08609     4.821
stenose100% (Occlusion)                                   1.417e+00  7.059e-01   0.08484    23.655
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             5.246e-07  1.906e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                     8.277e-01  1.208e+00   0.48049     1.426

Concordance= 0.682  (se = 0.037 )
Likelihood ratio test= 26.8  on 20 df,   p=0.1
Wald test            = 24.83  on 20 df,   p=0.2
Score (logrank) test = 26.17  on 20 df,   p=0.2


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.192191 
Standard error............: 0.284869 
Odds ratio (effect size)..: 1.212 
Lower 95% CI..............: 0.693 
Upper 95% CI..............: 2.118 
T-value...................: 0.674663 
P-value...................: 0.49989 
Sample size in model......: 970 
Number of events..........: 57 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 1001, number of events= 58 
   (1387 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148]  9.854e-02  1.104e+00  2.811e-01  0.351  0.72590   
Age                                                        5.007e-02  1.051e+00  1.795e-02  2.789  0.00529 **
Gendermale                                                 2.863e-01  1.331e+00  3.004e-01  0.953  0.34056   
Hypertension.compositeno                                  -2.175e-01  8.045e-01  4.449e-01 -0.489  0.62490   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                    -5.951e-02  9.422e-01  3.197e-01 -0.186  0.85233   
SmokerCurrentno                                           -3.434e-01  7.093e-01  2.943e-01 -1.167  0.24327   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                           3.975e-01  1.488e+00  2.915e-01  1.364  0.17269   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     3.438e-01  1.410e+00  3.744e-01  0.918  0.35853   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -5.790e-03  9.942e-01  7.008e-03 -0.826  0.40874   
BMI                                                        9.346e-02  1.098e+00  3.390e-02  2.757  0.00583 **
CAD_history                                               -5.704e-01  5.653e-01  3.291e-01 -1.734  0.08301 . 
Stroke_history                                             3.137e-01  1.368e+00  2.761e-01  1.136  0.25587   
Peripheral.interv                                          4.846e-01  1.624e+00  3.278e-01  1.478  0.13932   
stenose0-49%                                              -1.553e+01  1.806e-07  3.750e+03 -0.004  0.99670   
stenose50-70%                                             -5.725e-01  5.641e-01  1.161e+00 -0.493  0.62181   
stenose70-90%                                             -3.214e-01  7.252e-01  1.029e+00 -0.312  0.75480   
stenose90-99%                                             -3.196e-01  7.265e-01  1.029e+00 -0.311  0.75615   
stenose100% (Occlusion)                                    4.363e-01  1.547e+00  1.445e+00  0.302  0.76266   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                             -1.540e+01  2.048e-07  5.618e+03 -0.003  0.99781   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                     -1.713e-01  8.426e-01  2.758e-01 -0.621  0.53451   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.104e+00  9.062e-01   0.63612     1.914
Age                                                       1.051e+00  9.512e-01   1.01499     1.089
Gendermale                                                1.331e+00  7.511e-01   0.73900     2.399
Hypertension.compositeno                                  8.045e-01  1.243e+00   0.33641     1.924
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.422e-01  1.061e+00   0.50353     1.763
SmokerCurrentno                                           7.093e-01  1.410e+00   0.39840     1.263
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.488e+00  6.720e-01   0.84044     2.635
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.410e+00  7.091e-01   0.67701     2.938
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.942e-01  1.006e+00   0.98066     1.008
BMI                                                       1.098e+00  9.108e-01   1.02739     1.173
CAD_history                                               5.653e-01  1.769e+00   0.29660     1.077
Stroke_history                                            1.368e+00  7.307e-01   0.79658     2.351
Peripheral.interv                                         1.624e+00  6.159e-01   0.85395     3.087
stenose0-49%                                              1.806e-07  5.537e+06   0.00000       Inf
stenose50-70%                                             5.641e-01  1.773e+00   0.05800     5.486
stenose70-90%                                             7.252e-01  1.379e+00   0.09652     5.449
stenose90-99%                                             7.265e-01  1.377e+00   0.09666     5.460
stenose100% (Occlusion)                                   1.547e+00  6.464e-01   0.09112    26.266
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.048e-07  4.883e+06   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                     8.426e-01  1.187e+00   0.49076     1.447

Concordance= 0.684  (se = 0.036 )
Likelihood ratio test= 27.27  on 20 df,   p=0.1
Wald test            = 22.73  on 20 df,   p=0.3
Score (logrank) test = 26.34  on 20 df,   p=0.2


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epstroke.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.098544 
Standard error............: 0.28108 
Odds ratio (effect size)..: 1.104 
Lower 95% CI..............: 0.636 
Upper 95% CI..............: 1.915 
T-value...................: 0.350591 
P-value...................: 0.7258952 
Sample size in model......: 1001 
Number of events..........: 58 
* Analyzing the effect of plaque proteins on [epcoronary.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 368, number of events= 35 
   (2020 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694]  3.265e-02  1.033e+00  3.620e-01  0.090   0.9281  
Age                                                        4.434e-02  1.045e+00  2.524e-02  1.757   0.0790 .
Gendermale                                                 8.463e-01  2.331e+00  4.995e-01  1.694   0.0902 .
Hypertension.compositeno                                  -6.475e-01  5.233e-01  7.502e-01 -0.863   0.3881  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                     1.182e-01  1.125e+00  4.279e-01  0.276   0.7823  
SmokerCurrentno                                           -3.267e-01  7.213e-01  3.670e-01 -0.890   0.3734  
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA  
Med.Statin.LLDno                                           1.030e-01  1.109e+00  4.070e-01  0.253   0.8002  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     4.438e-01  1.559e+00  4.804e-01  0.924   0.3556  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -1.345e-02  9.866e-01  9.552e-03 -1.408   0.1591  
BMI                                                       -2.755e-02  9.728e-01  5.681e-02 -0.485   0.6277  
CAD_history                                                7.157e-01  2.046e+00  3.731e-01  1.918   0.0551 .
Stroke_history                                            -5.258e-01  5.911e-01  4.121e-01 -1.276   0.2020  
Peripheral.interv                                          2.659e-01  1.305e+00  4.098e-01  0.649   0.5164  
stenose0-49%                                              -4.150e-01  6.604e-01  9.455e+03  0.000   1.0000  
stenose50-70%                                              1.616e+01  1.039e+07  5.837e+03  0.003   0.9978  
stenose70-90%                                              1.617e+01  1.056e+07  5.837e+03  0.003   0.9978  
stenose90-99%                                              1.616e+01  1.046e+07  5.837e+03  0.003   0.9978  
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
IL6_pg_ug_2015_rank                                        1.736e-01  1.190e+00  1.820e-01  0.954   0.3400  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 1.033e+00  9.679e-01    0.5082     2.100
Age                                                       1.045e+00  9.566e-01    0.9949     1.098
Gendermale                                                2.331e+00  4.290e-01    0.8758     6.204
Hypertension.compositeno                                  5.233e-01  1.911e+00    0.1203     2.277
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.125e+00  8.885e-01    0.4865     2.604
SmokerCurrentno                                           7.213e-01  1.386e+00    0.3513     1.481
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.109e+00  9.021e-01    0.4992     2.461
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.559e+00  6.416e-01    0.6079     3.996
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.866e-01  1.014e+00    0.9683     1.005
BMI                                                       9.728e-01  1.028e+00    0.8703     1.087
CAD_history                                               2.046e+00  4.888e-01    0.9846     4.250
Stroke_history                                            5.911e-01  1.692e+00    0.2636     1.326
Peripheral.interv                                         1.305e+00  7.665e-01    0.5844     2.913
stenose0-49%                                              6.604e-01  1.514e+00    0.0000       Inf
stenose50-70%                                             1.039e+07  9.628e-08    0.0000       Inf
stenose70-90%                                             1.056e+07  9.465e-08    0.0000       Inf
stenose90-99%                                             1.046e+07  9.562e-08    0.0000       Inf
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank                                       1.190e+00  8.406e-01    0.8327     1.699

Concordance= 0.769  (se = 0.037 )
Likelihood ratio test= 29.04  on 18 df,   p=0.05
Wald test            = 19.05  on 18 df,   p=0.4
Score (logrank) test = 30.5  on 18 df,   p=0.03


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: IL6_rank 
Effect size...............: 0.032645 
Standard error............: 0.361965 
Odds ratio (effect size)..: 1.033 
Lower 95% CI..............: 0.508 
Upper 95% CI..............: 2.1 
T-value...................: 0.09019 
P-value...................: 0.9281365 
Sample size in model......: 368 
Number of events..........: 35 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 392, number of events= 36 
   (1996 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)  
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -1.373e-02  9.864e-01  3.585e-01 -0.038   0.9694  
Age                                                        3.619e-02  1.037e+00  2.487e-02  1.455   0.1455  
Gendermale                                                 6.864e-01  1.987e+00  4.617e-01  1.487   0.1371  
Hypertension.compositeno                                  -2.809e-01  7.551e-01  6.331e-01 -0.444   0.6572  
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA  
DiabetesStatusDiabetes                                     2.804e-01  1.324e+00  3.981e-01  0.704   0.4812  
SmokerCurrentno                                           -3.924e-01  6.754e-01  3.591e-01 -1.093   0.2745  
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA  
Med.Statin.LLDno                                           1.115e-01  1.118e+00  4.062e-01  0.274   0.7837  
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA  
Med.all.antiplateletno                                     4.027e-01  1.496e+00  4.781e-01  0.842   0.3995  
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA  
GFR_MDRD                                                  -1.685e-02  9.833e-01  9.425e-03 -1.788   0.0738 .
BMI                                                       -1.446e-02  9.856e-01  5.293e-02 -0.273   0.7848  
CAD_history                                                7.367e-01  2.089e+00  3.648e-01  2.020   0.0434 *
Stroke_history                                            -3.350e-01  7.153e-01  3.837e-01 -0.873   0.3826  
Peripheral.interv                                          3.051e-01  1.357e+00  4.015e-01  0.760   0.4474  
stenose0-49%                                              -4.331e-01  6.485e-01  9.334e+03  0.000   1.0000  
stenose50-70%                                              1.616e+01  1.042e+07  5.888e+03  0.003   0.9978  
stenose70-90%                                              1.607e+01  9.491e+06  5.888e+03  0.003   0.9978  
stenose90-99%                                              1.612e+01  1.004e+07  5.888e+03  0.003   0.9978  
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA  
stenoseNA                                                         NA         NA  0.000e+00     NA       NA  
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA  
stenose99                                                         NA         NA  0.000e+00     NA       NA  
IL6_pg_ug_2015_rank                                        2.116e-01  1.236e+00  1.763e-01  1.200   0.2301  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 9.864e-01  1.014e+00    0.4886     1.991
Age                                                       1.037e+00  9.645e-01    0.9875     1.089
Gendermale                                                1.987e+00  5.034e-01    0.8037     4.911
Hypertension.compositeno                                  7.551e-01  1.324e+00    0.2183     2.612
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.324e+00  7.555e-01    0.6066     2.888
SmokerCurrentno                                           6.754e-01  1.481e+00    0.3341     1.365
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.118e+00  8.945e-01    0.5043     2.478
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.496e+00  6.685e-01    0.5861     3.818
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.833e-01  1.017e+00    0.9653     1.002
BMI                                                       9.856e-01  1.015e+00    0.8885     1.093
CAD_history                                               2.089e+00  4.787e-01    1.0220     4.270
Stroke_history                                            7.153e-01  1.398e+00    0.3372     1.517
Peripheral.interv                                         1.357e+00  7.371e-01    0.6176     2.980
stenose0-49%                                              6.485e-01  1.542e+00    0.0000       Inf
stenose50-70%                                             1.042e+07  9.596e-08    0.0000       Inf
stenose70-90%                                             9.491e+06  1.054e-07    0.0000       Inf
stenose90-99%                                             1.004e+07  9.964e-08    0.0000       Inf
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank                                       1.236e+00  8.093e-01    0.8747     1.745

Concordance= 0.749  (se = 0.041 )
Likelihood ratio test= 27.64  on 18 df,   p=0.07
Wald test            = 16.45  on 18 df,   p=0.6
Score (logrank) test = 28.99  on 18 df,   p=0.05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.013729 
Standard error............: 0.358462 
Odds ratio (effect size)..: 0.986 
Lower 95% CI..............: 0.489 
Upper 95% CI..............: 1.991 
T-value...................: -0.038301 
P-value...................: 0.9694478 
Sample size in model......: 392 
Number of events..........: 36 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 1002, number of events= 79 
   (1386 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061]  5.813e-02  1.060e+00  2.304e-01  0.252  0.80083    
Age                                                       -3.043e-03  9.970e-01  1.510e-02 -0.201  0.84032    
Gendermale                                                 7.141e-01  2.042e+00  3.011e-01  2.372  0.01771 *  
Hypertension.compositeno                                  -7.882e-01  4.546e-01  5.252e-01 -1.501  0.13341    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -1.803e-01  8.351e-01  2.782e-01 -0.648  0.51711    
SmokerCurrentno                                           -5.750e-01  5.627e-01  2.428e-01 -2.369  0.01785 *  
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           2.493e-01  1.283e+00  2.741e-01  0.909  0.36312    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.737e-01  1.190e+00  3.354e-01  0.518  0.60448    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -2.040e-02  9.798e-01  5.967e-03 -3.419  0.00063 ***
BMI                                                        1.169e-02  1.012e+00  3.338e-02  0.350  0.72620    
CAD_history                                                9.403e-01  2.561e+00  2.410e-01  3.901 9.56e-05 ***
Stroke_history                                            -1.024e-01  9.027e-01  2.526e-01 -0.405  0.68527    
Peripheral.interv                                          4.301e-01  1.537e+00  2.613e-01  1.646  0.09979 .  
stenose0-49%                                              -1.581e+01  1.357e-07  3.608e+03 -0.004  0.99650    
stenose50-70%                                             -1.055e+00  3.483e-01  1.233e+00 -0.856  0.39220    
stenose70-90%                                             -1.716e-02  9.830e-01  1.022e+00 -0.017  0.98661    
stenose90-99%                                             -1.699e-01  8.437e-01  1.022e+00 -0.166  0.86794    
stenose100% (Occlusion)                                   -1.551e+01  1.841e-07  3.150e+03 -0.005  0.99607    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                              1.291e+00  3.637e+00  1.459e+00  0.885  0.37628    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.060e+00  9.435e-01   0.67470    1.6649
Age                                                       9.970e-01  1.003e+00   0.96789    1.0269
Gendermale                                                2.042e+00  4.896e-01   1.13195    3.6847
Hypertension.compositeno                                  4.546e-01  2.200e+00   0.16241    1.2727
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    8.351e-01  1.198e+00   0.48403    1.4407
SmokerCurrentno                                           5.627e-01  1.777e+00   0.34966    0.9055
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.283e+00  7.793e-01   0.74978    2.1959
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.190e+00  8.405e-01   0.61655    2.2958
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.798e-01  1.021e+00   0.96842    0.9913
BMI                                                       1.012e+00  9.884e-01   0.94768    1.0802
CAD_history                                               2.561e+00  3.905e-01   1.59668    4.1070
Stroke_history                                            9.027e-01  1.108e+00   0.55019    1.4810
Peripheral.interv                                         1.537e+00  6.504e-01   0.92119    2.5661
stenose0-49%                                              1.357e-07  7.370e+06   0.00000       Inf
stenose50-70%                                             3.483e-01  2.871e+00   0.03108    3.9018
stenose70-90%                                             9.830e-01  1.017e+00   0.13256    7.2891
stenose90-99%                                             8.437e-01  1.185e+00   0.11388    6.2514
stenose100% (Occlusion)                                   1.841e-07  5.432e+06   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             3.637e+00  2.750e-01   0.20825   63.5179
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                            NA         NA        NA        NA

Concordance= 0.738  (se = 0.027 )
Likelihood ratio test= 61.46  on 19 df,   p=2e-06
Wald test            = 60.06  on 19 df,   p=4e-06
Score (logrank) test = 64.83  on 19 df,   p=7e-07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: 0.058129 
Standard error............: 0.230422 
Odds ratio (effect size)..: 1.06 
Lower 95% CI..............: 0.675 
Upper 95% CI..............: 1.665 
T-value...................: 0.252272 
P-value...................: 0.8008308 
Sample size in model......: 1002 
Number of events..........: 79 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 970, number of events= 79 
   (1418 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]  2.941e-01  1.342e+00  2.501e-01  1.176 0.239562    
Age                                                       -2.902e-03  9.971e-01  1.518e-02 -0.191 0.848414    
Gendermale                                                 6.691e-01  1.953e+00  3.010e-01  2.223 0.026216 *  
Hypertension.compositeno                                  -7.890e-01  4.543e-01  5.262e-01 -1.499 0.133764    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -1.543e-01  8.570e-01  2.782e-01 -0.555 0.579160    
SmokerCurrentno                                           -5.523e-01  5.756e-01  2.444e-01 -2.259 0.023868 *  
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           1.992e-01  1.220e+00  2.773e-01  0.718 0.472584    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.835e-01  1.201e+00  3.363e-01  0.546 0.585317    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -1.969e-02  9.805e-01  5.957e-03 -3.306 0.000947 ***
BMI                                                        1.099e-02  1.011e+00  3.388e-02  0.324 0.745737    
CAD_history                                                9.533e-01  2.594e+00  2.411e-01  3.954 7.69e-05 ***
Stroke_history                                            -1.332e-01  8.753e-01  2.524e-01 -0.528 0.597671    
Peripheral.interv                                          3.997e-01  1.491e+00  2.606e-01  1.533 0.125201    
stenose0-49%                                              -1.580e+01  1.379e-07  4.034e+03 -0.004 0.996875    
stenose50-70%                                             -1.057e+00  3.475e-01  1.232e+00 -0.858 0.390766    
stenose70-90%                                             -9.532e-02  9.091e-01  1.024e+00 -0.093 0.925855    
stenose90-99%                                             -2.567e-01  7.736e-01  1.025e+00 -0.250 0.802245    
stenose100% (Occlusion)                                   -1.556e+01  1.749e-07  2.984e+03 -0.005 0.995840    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                              1.071e+00  2.920e+00  1.470e+00  0.729 0.465935    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                     -7.198e-02  9.305e-01  2.421e-01 -0.297 0.766252    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.342e+00  7.452e-01   0.82199    2.1907
Age                                                       9.971e-01  1.003e+00   0.96787    1.0272
Gendermale                                                1.953e+00  5.122e-01   1.08239    3.5222
Hypertension.compositeno                                  4.543e-01  2.201e+00   0.16197    1.2742
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    8.570e-01  1.167e+00   0.49684    1.4784
SmokerCurrentno                                           5.756e-01  1.737e+00   0.35652    0.9295
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.220e+00  8.194e-01   0.70873    2.1014
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.201e+00  8.324e-01   0.62153    2.3222
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.805e-01  1.020e+00   0.96912    0.9920
BMI                                                       1.011e+00  9.891e-01   0.94609    1.0805
CAD_history                                               2.594e+00  3.855e-01   1.61726    4.1613
Stroke_history                                            8.753e-01  1.142e+00   0.53373    1.4354
Peripheral.interv                                         1.491e+00  6.706e-01   0.89475    2.4856
stenose0-49%                                              1.379e-07  7.253e+06   0.00000       Inf
stenose50-70%                                             3.475e-01  2.878e+00   0.03108    3.8845
stenose70-90%                                             9.091e-01  1.100e+00   0.12210    6.7683
stenose90-99%                                             7.736e-01  1.293e+00   0.10374    5.7687
stenose100% (Occlusion)                                   1.749e-07  5.719e+06   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             2.920e+00  3.425e-01   0.16384   52.0301
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                     9.305e-01  1.075e+00   0.57893    1.4957

Concordance= 0.736  (se = 0.028 )
Likelihood ratio test= 61.86  on 20 df,   p=4e-06
Wald test            = 61  on 20 df,   p=5e-06
Score (logrank) test = 66.22  on 20 df,   p=7e-07


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.294099 
Standard error............: 0.250066 
Odds ratio (effect size)..: 1.342 
Lower 95% CI..............: 0.822 
Upper 95% CI..............: 2.191 
T-value...................: 1.176083 
P-value...................: 0.2395617 
Sample size in model......: 970 
Number of events..........: 79 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 1001, number of events= 79 
   (1387 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148]  1.074e-02  1.011e+00  2.418e-01  0.044 0.964569    
Age                                                       -3.637e-03  9.964e-01  1.513e-02 -0.240 0.809994    
Gendermale                                                 7.173e-01  2.049e+00  3.011e-01  2.382 0.017207 *  
Hypertension.compositeno                                  -7.921e-01  4.529e-01  5.256e-01 -1.507 0.131811    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -1.848e-01  8.313e-01  2.784e-01 -0.664 0.506811    
SmokerCurrentno                                           -5.678e-01  5.668e-01  2.429e-01 -2.338 0.019394 *  
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           2.635e-01  1.302e+00  2.744e-01  0.960 0.336808    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.715e-01  1.187e+00  3.354e-01  0.512 0.609000    
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -2.055e-02  9.797e-01  5.981e-03 -3.436 0.000589 ***
BMI                                                        1.214e-02  1.012e+00  3.335e-02  0.364 0.715939    
CAD_history                                                9.475e-01  2.579e+00  2.413e-01  3.927  8.6e-05 ***
Stroke_history                                            -1.057e-01  8.997e-01  2.527e-01 -0.418 0.675695    
Peripheral.interv                                          4.261e-01  1.531e+00  2.613e-01  1.631 0.102925    
stenose0-49%                                              -1.580e+01  1.370e-07  3.598e+03 -0.004 0.996495    
stenose50-70%                                             -1.058e+00  3.471e-01  1.234e+00 -0.857 0.391189    
stenose70-90%                                             -1.794e-02  9.822e-01  1.025e+00 -0.017 0.986043    
stenose90-99%                                             -1.636e-01  8.490e-01  1.026e+00 -0.160 0.873227    
stenose100% (Occlusion)                                   -1.550e+01  1.848e-07  3.151e+03 -0.005 0.996074    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                              1.266e+00  3.545e+00  1.462e+00  0.865 0.386797    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                      4.822e-02  1.049e+00  2.419e-01  0.199 0.841964    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 1.011e+00  9.893e-01   0.62928    1.6236
Age                                                       9.964e-01  1.004e+00   0.96726    1.0264
Gendermale                                                2.049e+00  4.881e-01   1.13560    3.6969
Hypertension.compositeno                                  4.529e-01  2.208e+00   0.16166    1.2688
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    8.313e-01  1.203e+00   0.48168    1.4346
SmokerCurrentno                                           5.668e-01  1.764e+00   0.35208    0.9123
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.302e+00  7.683e-01   0.76016    2.2285
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.187e+00  8.424e-01   0.61524    2.2906
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.797e-01  1.021e+00   0.96824    0.9912
BMI                                                       1.012e+00  9.879e-01   0.94816    1.0806
CAD_history                                               2.579e+00  3.877e-01   1.60738    4.1388
Stroke_history                                            8.997e-01  1.112e+00   0.54825    1.4764
Peripheral.interv                                         1.531e+00  6.531e-01   0.91761    2.5552
stenose0-49%                                              1.370e-07  7.301e+06   0.00000       Inf
stenose50-70%                                             3.471e-01  2.881e+00   0.03091    3.8982
stenose70-90%                                             9.822e-01  1.018e+00   0.13167    7.3272
stenose90-99%                                             8.490e-01  1.178e+00   0.11375    6.3373
stenose100% (Occlusion)                                   1.848e-07  5.412e+06   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             3.545e+00  2.821e-01   0.20178   62.2819
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                     1.049e+00  9.529e-01   0.65322    1.6859

Concordance= 0.739  (se = 0.027 )
Likelihood ratio test= 61.78  on 20 df,   p=4e-06
Wald test            = 60.46  on 20 df,   p=6e-06
Score (logrank) test = 65.18  on 20 df,   p=1e-06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcoronary.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: 0.010741 
Standard error............: 0.241803 
Odds ratio (effect size)..: 1.011 
Lower 95% CI..............: 0.629 
Upper 95% CI..............: 1.624 
T-value...................: 0.044421 
P-value...................: 0.9645686 
Sample size in model......: 1001 
Number of events..........: 79 
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
 - creating temporary SE for this work.
 - making a 'Surv' object and adding this to temporary dataframe.
 - making strata of each of the plaque proteins and start survival analysis.
   > processing [IL6_rank]; 1 out of 5 proteins.
   > cross tabulation of IL6_rank-stratum.

[-1.48329,0.00474) [ 0.00474,3.10694] 
               265                264 

   > fitting the model for IL6_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 368, number of events= 16 
   (2020 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] -5.348e-01  5.858e-01  5.697e-01 -0.939  0.34787   
Age                                                        1.204e-01  1.128e+00  4.428e-02  2.719  0.00656 **
Gendermale                                                 1.300e+00  3.668e+00  8.109e-01  1.603  0.10901   
Hypertension.compositeno                                  -1.825e+01  1.186e-08  6.198e+03 -0.003  0.99765   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     6.644e-01  1.943e+00  6.672e-01  0.996  0.31935   
SmokerCurrentno                                           -4.135e-01  6.613e-01  5.625e-01 -0.735  0.46231   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                           6.205e-01  1.860e+00  5.885e-01  1.054  0.29169   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     5.564e-01  1.744e+00  7.174e-01  0.775  0.43805   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -2.473e-02  9.756e-01  1.398e-02 -1.769  0.07689 . 
BMI                                                        3.300e-02  1.034e+00  8.036e-02  0.411  0.68131   
CAD_history                                                1.119e-01  1.118e+00  5.550e-01  0.202  0.84020   
Stroke_history                                             6.650e-02  1.069e+00  5.801e-01  0.115  0.90875   
Peripheral.interv                                          1.094e-01  1.116e+00  7.207e-01  0.152  0.87939   
stenose0-49%                                              -1.205e-01  8.865e-01  4.082e+04  0.000  1.00000   
stenose50-70%                                              6.892e-01  1.992e+00  2.304e+04  0.000  0.99998   
stenose70-90%                                              1.854e+01  1.126e+08  2.053e+04  0.001  0.99928   
stenose90-99%                                              1.825e+01  8.401e+07  2.053e+04  0.001  0.99929   
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
IL6_pg_ug_2015_rank                                        1.903e-01  1.210e+00  2.990e-01  0.636  0.52458   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00474,3.10694] 5.858e-01  1.707e+00    0.1918     1.789
Age                                                       1.128e+00  8.866e-01    1.0342     1.230
Gendermale                                                3.668e+00  2.727e-01    0.7485    17.973
Hypertension.compositeno                                  1.186e-08  8.434e+07    0.0000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.943e+00  5.146e-01    0.5256     7.185
SmokerCurrentno                                           6.613e-01  1.512e+00    0.2196     1.992
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.860e+00  5.377e-01    0.5869     5.894
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.744e+00  5.733e-01    0.4275     7.117
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.756e-01  1.025e+00    0.9492     1.003
BMI                                                       1.034e+00  9.675e-01    0.8829     1.210
CAD_history                                               1.118e+00  8.941e-01    0.3768     3.319
Stroke_history                                            1.069e+00  9.357e-01    0.3428     3.332
Peripheral.interv                                         1.116e+00  8.964e-01    0.2716     4.581
stenose0-49%                                              8.865e-01  1.128e+00    0.0000       Inf
stenose50-70%                                             1.992e+00  5.020e-01    0.0000       Inf
stenose70-90%                                             1.126e+08  8.880e-09    0.0000       Inf
stenose90-99%                                             8.401e+07  1.190e-08    0.0000       Inf
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank                                       1.210e+00  8.267e-01    0.6731     2.173

Concordance= 0.874  (se = 0.028 )
Likelihood ratio test= 31.1  on 18 df,   p=0.03
Wald test            = 7.85  on 18 df,   p=1
Score (logrank) test = 27.82  on 18 df,   p=0.06


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: IL6_rank 
Effect size...............: -0.534802 
Standard error............: 0.569715 
Odds ratio (effect size)..: 0.586 
Lower 95% CI..............: 0.192 
Upper 95% CI..............: 1.789 
T-value...................: -0.938719 
P-value...................: 0.3478749 
Sample size in model......: 368 
Number of events..........: 16 
   > processing [MCP1_rank]; 2 out of 5 proteins.
   > cross tabulation of MCP1_rank-stratum.

[-2.41053,0.00444) [ 0.00444,3.12635] 
               283                282 

   > fitting the model for MCP1_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 392, number of events= 17 
   (1996 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] -3.168e-01  7.285e-01  5.580e-01 -0.568  0.57023   
Age                                                        1.065e-01  1.112e+00  4.140e-02  2.572  0.01012 * 
Gendermale                                                 1.261e+00  3.527e+00  7.908e-01  1.594  0.11092   
Hypertension.compositeno                                  -1.930e+01  4.153e-09  8.188e+03 -0.002  0.99812   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     5.345e-01  1.707e+00  6.662e-01  0.802  0.42240   
SmokerCurrentno                                           -4.488e-01  6.384e-01  5.490e-01 -0.817  0.41367   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                           7.274e-01  2.070e+00  5.707e-01  1.275  0.20245   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     3.782e-01  1.460e+00  7.117e-01  0.531  0.59515   
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -3.813e-02  9.626e-01  1.433e-02 -2.662  0.00778 **
BMI                                                        1.193e-02  1.012e+00  7.964e-02  0.150  0.88096   
CAD_history                                               -3.662e-01  6.934e-01  5.744e-01 -0.637  0.52382   
Stroke_history                                             1.794e-01  1.196e+00  5.583e-01  0.321  0.74803   
Peripheral.interv                                          6.105e-01  1.841e+00  6.483e-01  0.942  0.34637   
stenose0-49%                                              -5.579e-01  5.724e-01  7.733e+04  0.000  0.99999   
stenose50-70%                                              6.658e-01  1.946e+00  4.168e+04  0.000  0.99999   
stenose70-90%                                              1.967e+01  3.486e+08  3.799e+04  0.001  0.99959   
stenose90-99%                                              1.929e+01  2.375e+08  3.799e+04  0.001  0.99960   
stenose100% (Occlusion)                                           NA         NA  0.000e+00     NA       NA   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
IL6_pg_ug_2015_rank                                        9.400e-02  1.099e+00  2.942e-01  0.319  0.74936   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00444,3.12635] 7.285e-01  1.373e+00    0.2440     2.175
Age                                                       1.112e+00  8.990e-01    1.0256     1.206
Gendermale                                                3.527e+00  2.835e-01    0.7487    16.619
Hypertension.compositeno                                  4.153e-09  2.408e+08    0.0000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.707e+00  5.860e-01    0.4624     6.298
SmokerCurrentno                                           6.384e-01  1.566e+00    0.2177     1.872
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          2.070e+00  4.831e-01    0.6763     6.335
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    1.460e+00  6.851e-01    0.3618     5.889
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.626e-01  1.039e+00    0.9359     0.990
BMI                                                       1.012e+00  9.881e-01    0.8657     1.183
CAD_history                                               6.934e-01  1.442e+00    0.2249     2.138
Stroke_history                                            1.196e+00  8.358e-01    0.4005     3.574
Peripheral.interv                                         1.841e+00  5.431e-01    0.5167     6.562
stenose0-49%                                              5.724e-01  1.747e+00    0.0000       Inf
stenose50-70%                                             1.946e+00  5.138e-01    0.0000       Inf
stenose70-90%                                             3.486e+08  2.868e-09    0.0000       Inf
stenose90-99%                                             2.375e+08  4.210e-09    0.0000       Inf
stenose100% (Occlusion)                                          NA         NA        NA        NA
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                                    NA         NA        NA        NA
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank                                       1.099e+00  9.103e-01    0.6171     1.956

Concordance= 0.89  (se = 0.023 )
Likelihood ratio test= 35.41  on 18 df,   p=0.008
Wald test            = 6.69  on 18 df,   p=1
Score (logrank) test = 31.24  on 18 df,   p=0.03


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_rank 
Effect size...............: -0.316806 
Standard error............: 0.55804 
Odds ratio (effect size)..: 0.728 
Lower 95% CI..............: 0.244 
Upper 95% CI..............: 2.175 
T-value...................: -0.567713 
P-value...................: 0.5702299 
Sample size in model......: 392 
Number of events..........: 17 
   > processing [IL6_pg_ug_2015_rank]; 3 out of 5 proteins.
   > cross tabulation of IL6_pg_ug_2015_rank-stratum.

[-3.33061,0.00109) [ 0.00109,3.33061] 
               577                577 

   > fitting the model for IL6_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 1002, number of events= 35 
   (1386 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061]  3.124e-01  1.367e+00  3.495e-01  0.894 0.371352    
Age                                                        6.928e-02  1.072e+00  2.589e-02  2.676 0.007450 ** 
Gendermale                                                 1.013e+00  2.755e+00  4.933e-01  2.054 0.039942 *  
Hypertension.compositeno                                  -1.767e+01  2.123e-08  3.770e+03 -0.005 0.996261    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -5.545e-02  9.461e-01  4.082e-01 -0.136 0.891958    
SmokerCurrentno                                           -4.117e-01  6.625e-01  3.828e-01 -1.076 0.282106    
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           3.902e-02  1.040e+00  4.239e-01  0.092 0.926647    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.096e+00  2.991e+00  3.963e-01  2.765 0.005696 ** 
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -3.121e-02  9.693e-01  9.102e-03 -3.428 0.000607 ***
BMI                                                        6.702e-02  1.069e+00  5.147e-02  1.302 0.192850    
CAD_history                                                2.453e-01  1.278e+00  3.548e-01  0.692 0.489238    
Stroke_history                                            -1.405e-01  8.689e-01  3.867e-01 -0.363 0.716259    
Peripheral.interv                                          6.223e-01  1.863e+00  4.073e-01  1.528 0.126503    
stenose0-49%                                              -1.957e+01  3.162e-09  2.801e+04 -0.001 0.999442    
stenose50-70%                                             -8.921e-01  4.098e-01  1.238e+00 -0.720 0.471253    
stenose70-90%                                             -1.380e+00  2.515e-01  1.065e+00 -1.296 0.195019    
stenose90-99%                                             -8.273e-01  4.372e-01  1.042e+00 -0.794 0.427146    
stenose100% (Occlusion)                                   -1.912e+01  4.954e-09  1.969e+04 -0.001 0.999225    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.913e+01  4.932e-09  4.806e+04  0.000 0.999682    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                             NA         NA  0.000e+00     NA       NA    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00109,3.33061] 1.367e+00  7.317e-01   0.68899    2.7110
Age                                                       1.072e+00  9.331e-01   1.01871    1.1275
Gendermale                                                2.755e+00  3.630e-01   1.04766    7.2440
Hypertension.compositeno                                  2.123e-08  4.709e+07   0.00000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.461e-01  1.057e+00   0.42503    2.1058
SmokerCurrentno                                           6.625e-01  1.509e+00   0.31288    1.4029
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.040e+00  9.617e-01   0.45305    2.3865
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    2.991e+00  3.343e-01   1.37565    6.5034
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.693e-01  1.032e+00   0.95214    0.9867
BMI                                                       1.069e+00  9.352e-01   0.96671    1.1828
CAD_history                                               1.278e+00  7.824e-01   0.63761    2.5618
Stroke_history                                            8.689e-01  1.151e+00   0.40722    1.8539
Peripheral.interv                                         1.863e+00  5.367e-01   0.83869    4.1396
stenose0-49%                                              3.162e-09  3.162e+08   0.00000       Inf
stenose50-70%                                             4.098e-01  2.440e+00   0.03619    4.6407
stenose70-90%                                             2.515e-01  3.976e+00   0.03118    2.0286
stenose90-99%                                             4.372e-01  2.287e+00   0.05675    3.3690
stenose100% (Occlusion)                                   4.954e-09  2.018e+08   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             4.932e-09  2.028e+08   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                            NA         NA        NA        NA

Concordance= 0.841  (se = 0.028 )
Likelihood ratio test= 59.89  on 19 df,   p=4e-06
Wald test            = 21.5  on 19 df,   p=0.3
Score (logrank) test = 56.6  on 19 df,   p=1e-05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: IL6_pg_ug_2015_rank 
Effect size...............: 0.312398 
Standard error............: 0.349461 
Odds ratio (effect size)..: 1.367 
Lower 95% CI..............: 0.689 
Upper 95% CI..............: 2.711 
T-value...................: 0.893944 
P-value...................: 0.3713521 
Sample size in model......: 1002 
Number of events..........: 35 
   > processing [IL6R_pg_ug_2015_rank]; 4 out of 5 proteins.
   > cross tabulation of IL6R_pg_ug_2015_rank-stratum.

[-3.33109,0.00108) [ 0.00108,3.33109] 
               578                578 

   > fitting the model for IL6R_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 970, number of events= 35 
   (1418 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)   
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109]  4.835e-01  1.622e+00  3.875e-01  1.248  0.21215   
Age                                                        6.968e-02  1.072e+00  2.613e-02  2.667  0.00766 **
Gendermale                                                 9.835e-01  2.674e+00  4.923e-01  1.998  0.04576 * 
Hypertension.compositeno                                  -1.769e+01  2.076e-08  3.845e+03 -0.005  0.99633   
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA   
DiabetesStatusDiabetes                                     1.194e-02  1.012e+00  4.099e-01  0.029  0.97676   
SmokerCurrentno                                           -3.921e-01  6.757e-01  3.837e-01 -1.022  0.30689   
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA   
Med.Statin.LLDno                                          -4.238e-02  9.585e-01  4.273e-01 -0.099  0.92100   
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA   
Med.all.antiplateletno                                     1.090e+00  2.973e+00  3.960e-01  2.752  0.00593 **
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA   
GFR_MDRD                                                  -2.995e-02  9.705e-01  9.116e-03 -3.285  0.00102 **
BMI                                                        6.719e-02  1.069e+00  5.221e-02  1.287  0.19817   
CAD_history                                                2.806e-01  1.324e+00  3.558e-01  0.789  0.43026   
Stroke_history                                            -1.626e-01  8.499e-01  3.863e-01 -0.421  0.67387   
Peripheral.interv                                          5.694e-01  1.767e+00  4.049e-01  1.406  0.15964   
stenose0-49%                                              -1.927e+01  4.275e-09  3.249e+04 -0.001  0.99953   
stenose50-70%                                             -9.251e-01  3.965e-01  1.238e+00 -0.747  0.45499   
stenose70-90%                                             -1.478e+00  2.280e-01  1.066e+00 -1.386  0.16572   
stenose90-99%                                             -9.727e-01  3.781e-01  1.049e+00 -0.927  0.35383   
stenose100% (Occlusion)                                   -1.925e+01  4.363e-09  2.128e+04 -0.001  0.99928   
stenoseNA                                                         NA         NA  0.000e+00     NA       NA   
stenose50-99%                                             -1.928e+01  4.238e-09  4.922e+04  0.000  0.99969   
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA   
stenose99                                                         NA         NA  0.000e+00     NA       NA   
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                      1.342e-01  1.144e+00  3.690e-01  0.364  0.71610   
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00108,3.33109] 1.622e+00  6.166e-01   0.75879     3.466
Age                                                       1.072e+00  9.327e-01   1.01864     1.128
Gendermale                                                2.674e+00  3.740e-01   1.01868     7.018
Hypertension.compositeno                                  2.076e-08  4.816e+07   0.00000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    1.012e+00  9.881e-01   0.45320     2.260
SmokerCurrentno                                           6.757e-01  1.480e+00   0.31850     1.433
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          9.585e-01  1.043e+00   0.41481     2.215
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    2.973e+00  3.363e-01   1.36823     6.462
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.705e-01  1.030e+00   0.95331     0.988
BMI                                                       1.069e+00  9.350e-01   0.96546     1.185
CAD_history                                               1.324e+00  7.553e-01   0.65921     2.659
Stroke_history                                            8.499e-01  1.177e+00   0.39860     1.812
Peripheral.interv                                         1.767e+00  5.659e-01   0.79917     3.908
stenose0-49%                                              4.275e-09  2.339e+08   0.00000       Inf
stenose50-70%                                             3.965e-01  2.522e+00   0.03502     4.489
stenose70-90%                                             2.280e-01  4.385e+00   0.02820     1.844
stenose90-99%                                             3.781e-01  2.645e+00   0.04837     2.955
stenose100% (Occlusion)                                   4.363e-09  2.292e+08   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             4.238e-09  2.360e+08   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                     1.144e+00  8.744e-01   0.55483     2.357

Concordance= 0.838  (se = 0.03 )
Likelihood ratio test= 60.45  on 20 df,   p=6e-06
Wald test            = 22.49  on 20 df,   p=0.3
Score (logrank) test = 58.56  on 20 df,   p=1e-05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' IL6R_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: IL6R_pg_ug_2015_rank 
Effect size...............: 0.483527 
Standard error............: 0.387539 
Odds ratio (effect size)..: 1.622 
Lower 95% CI..............: 0.759 
Upper 95% CI..............: 3.466 
T-value...................: 1.247686 
P-value...................: 0.2121461 
Sample size in model......: 970 
Number of events..........: 35 
   > processing [MCP1_pg_ug_2015_rank]; 5 out of 5 proteins.
   > cross tabulation of MCP1_pg_ug_2015_rank-stratum.

[-3.34148,0.00104) [ 0.00104,3.34148] 
               600                600 

   > fitting the model for MCP1_pg_ug_2015_rank-stratum.

   > make a Kaplan-Meier-shizzle...

   > perform the Cox-regression fashizzle and plot it...

Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] + 
    Age + Gender + Hypertension.composite + DiabetesStatus + 
    SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + 
    BMI + CAD_history + Stroke_history + Peripheral.interv + 
    stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)

  n= 1001, number of events= 35 
   (1387 observations deleted due to missingness)

                                                                coef  exp(coef)   se(coef)      z Pr(>|z|)    
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] -1.311e-01  8.771e-01  3.603e-01 -0.364 0.715928    
Age                                                        6.942e-02  1.072e+00  2.601e-02  2.669 0.007604 ** 
Gendermale                                                 1.020e+00  2.774e+00  4.935e-01  2.067 0.038694 *  
Hypertension.compositeno                                  -1.766e+01  2.148e-08  3.772e+03 -0.005 0.996265    
Hypertension.compositeyes                                         NA         NA  0.000e+00     NA       NA    
DiabetesStatusDiabetes                                    -6.257e-02  9.393e-01  4.085e-01 -0.153 0.878241    
SmokerCurrentno                                           -4.086e-01  6.646e-01  3.830e-01 -1.067 0.285998    
SmokerCurrentyes                                                  NA         NA  0.000e+00     NA       NA    
Med.Statin.LLDno                                           3.815e-02  1.039e+00  4.251e-01  0.090 0.928495    
Med.Statin.LLDyes                                                 NA         NA  0.000e+00     NA       NA    
Med.all.antiplateletno                                     1.103e+00  3.012e+00  3.970e-01  2.777 0.005479 ** 
Med.all.antiplateletyes                                           NA         NA  0.000e+00     NA       NA    
GFR_MDRD                                                  -3.125e-02  9.692e-01  9.152e-03 -3.415 0.000638 ***
BMI                                                        6.593e-02  1.068e+00  5.110e-02  1.290 0.197019    
CAD_history                                                2.433e-01  1.275e+00  3.554e-01  0.685 0.493616    
Stroke_history                                            -1.326e-01  8.758e-01  3.875e-01 -0.342 0.732094    
Peripheral.interv                                          6.167e-01  1.853e+00  4.071e-01  1.515 0.129810    
stenose0-49%                                              -1.966e+01  2.886e-09  2.782e+04 -0.001 0.999436    
stenose50-70%                                             -9.291e-01  3.949e-01  1.242e+00 -0.748 0.454465    
stenose70-90%                                             -1.429e+00  2.395e-01  1.073e+00 -1.331 0.183035    
stenose90-99%                                             -8.774e-01  4.159e-01  1.051e+00 -0.835 0.403982    
stenose100% (Occlusion)                                   -1.920e+01  4.589e-09  1.973e+04 -0.001 0.999224    
stenoseNA                                                         NA         NA  0.000e+00     NA       NA    
stenose50-99%                                             -1.909e+01  5.110e-09  4.798e+04  0.000 0.999683    
stenose70-99%                                                     NA         NA  0.000e+00     NA       NA    
stenose99                                                         NA         NA  0.000e+00     NA       NA    
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                      3.461e-01  1.414e+00  3.645e-01  0.949 0.342382    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

                                                          exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00104,3.34148] 8.771e-01  1.140e+00   0.43288    1.7773
Age                                                       1.072e+00  9.329e-01   1.01862    1.1279
Gendermale                                                2.774e+00  3.605e-01   1.05447    7.2981
Hypertension.compositeno                                  2.148e-08  4.655e+07   0.00000       Inf
Hypertension.compositeyes                                        NA         NA        NA        NA
DiabetesStatusDiabetes                                    9.393e-01  1.065e+00   0.42184    2.0917
SmokerCurrentno                                           6.646e-01  1.505e+00   0.31375    1.4077
SmokerCurrentyes                                                 NA         NA        NA        NA
Med.Statin.LLDno                                          1.039e+00  9.626e-01   0.45157    2.3901
Med.Statin.LLDyes                                                NA         NA        NA        NA
Med.all.antiplateletno                                    3.012e+00  3.320e-01   1.38341    6.5594
Med.all.antiplateletyes                                          NA         NA        NA        NA
GFR_MDRD                                                  9.692e-01  1.032e+00   0.95200    0.9868
BMI                                                       1.068e+00  9.362e-01   0.96635    1.1807
CAD_history                                               1.275e+00  7.840e-01   0.63554    2.5597
Stroke_history                                            8.758e-01  1.142e+00   0.40980    1.8716
Peripheral.interv                                         1.853e+00  5.397e-01   0.83427    4.1144
stenose0-49%                                              2.886e-09  3.465e+08   0.00000       Inf
stenose50-70%                                             3.949e-01  2.532e+00   0.03461    4.5063
stenose70-90%                                             2.395e-01  4.175e+00   0.02922    1.9633
stenose90-99%                                             4.159e-01  2.405e+00   0.05297    3.2650
stenose100% (Occlusion)                                   4.589e-09  2.179e+08   0.00000       Inf
stenoseNA                                                        NA         NA        NA        NA
stenose50-99%                                             5.110e-09  1.957e+08   0.00000       Inf
stenose70-99%                                                    NA         NA        NA        NA
stenose99                                                        NA         NA        NA        NA
IL6_pg_ug_2015_rank[ 0.00109,3.33061]                     1.414e+00  7.074e-01   0.69187    2.8881

Concordance= 0.841  (se = 0.028 )
Likelihood ratio test= 60.03  on 20 df,   p=7e-06
Wald test            = 21.58  on 20 df,   p=0.4
Score (logrank) test = 56.64  on 20 df,   p=2e-05


   > writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.

We have collected the following:
Dataset used..............: AEDB.CEA 
Outcome analyzed..........: epcvdeath.3years 
Protein...................: MCP1_pg_ug_2015_rank 
Effect size...............: -0.131118 
Standard error............: 0.360306 
Odds ratio (effect size)..: 0.877 
Lower 95% CI..............: 0.433 
Upper 95% CI..............: 1.777 
T-value...................: -0.363906 
P-value...................: 0.715928 
Sample size in model......: 1001 
Number of events..........: 35 

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL4.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)
object 'head.style' not found

Correlations

All biomarkers

We correlated serum and plaque levels of the biomarkers.


# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")
Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
  Use `force = TRUE` to force installation
library(ggcorrplot)

# Creating matrix - natural log transformed
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN",
                                     TRAITS.BIN, TRAITS.CON)
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   2388 obs. of  12 variables:
 $ IL6_LN             : num  3.97 4.56 4.58 4.93 NA ...
 $ MCP1_LN            : num  3.7 5.53 4.35 4.19 NA ...
 $ IL6_pg_ug_2015_LN  : num  -5.23 -7.65 -2 NA NA ...
 $ MCP1_pg_ug_2015_LN : num  -0.0505 NA 0.8335 NA NA ...
 $ IL6R_pg_ug_2015_LN : num  0.0893 -8.8939 -0.3091 NA NA ...
 $ CalcificationPlaque: num  1 2 2 2 1 1 2 1 1 2 ...
 $ CollagenPlaque     : num  2 2 2 2 2 2 1 1 2 1 ...
 $ Fat10Perc          : num  2 2 1 1 2 2 2 2 2 2 ...
 $ IPH                : num  2 1 2 2 2 2 2 2 1 2 ...
 $ Macrophages_LN     : num  0.542 -1.732 -1.386 -0.426 -4.605 ...
 $ SMC_LN             : num  1.411 1.833 0.965 0.55 2.239 ...
 $ VesselDensity_LN   : num  0.846 1.203 2.46 2.398 1.541 ...
AEDB.CEA.matrix <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers <- round(cor(AEDB.CEA.matrix, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers

corr_biomarkers_p <- ggcorrplot::cor_pmat(AEDB.CEA.matrix, use = "pairwise.complete.obs", method = "spearman")
Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
                                               TRAITS.BIN, TRAITS.CON.RANK)
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)
Classes ‘tbl_df’, ‘tbl’ and 'data.frame':   2388 obs. of  12 variables:
 $ IL6_rank            : num  0.205 0.703 0.746 1.085 NA ...
 $ MCP1_rank           : num  -0.944 1.196 -0.297 -0.499 NA ...
 $ IL6_pg_ug_2015_rank : num  -1.422 -2.744 0.919 NA NA ...
 $ MCP1_pg_ug_2015_rank: num  0.936 NA 1.691 NA NA ...
 $ IL6R_pg_ug_2015_rank: num  2.21 -2.85 1.78 NA NA ...
 $ CalcificationPlaque : num  1 2 2 2 1 1 2 1 1 2 ...
 $ CollagenPlaque      : num  2 2 2 2 2 2 1 1 2 1 ...
 $ Fat10Perc           : num  2 2 1 1 2 2 2 2 2 2 ...
 $ IPH                 : num  2 1 2 2 2 2 2 2 1 2 ...
 $ Macrophages_rank    : num  1.12 -0.276 -0.105 0.403 -1.414 ...
 $ SMC_rank            : num  1.134 1.678 0.625 0.256 2.086 ...
 $ VesselDensity_rank  : num  -0.976 -0.773 0.713 0.609 -0.529 ...
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")
Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))



# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.df <- as.data.table(flattenCorrMatrix(corr_biomarkers, corr_biomarkers_p))
DT::datatable(corr_biomarkers.df)


corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)


# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AEDB.CEA.matrix, method = "spearman", histogram = TRUE, pch = 3)


chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)



# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA, 
        columns = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK), 
        columnLabels = c("IL6 (serum)", "MCP1 (serum)", "IL6", "MCP1", "IL6R",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 
Extra arguments: 'method' are being ignored.  If these are meant to be aesthetics, submit them using the 'mapping' variable within ggpairs with ggplot2::aes or ggplot2::aes_string.

Circulating MCP1

Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:

  1. A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
  2. Associations of circulating MCP-1 levels with plaque vulnerability characteristics
  3. Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
  4. Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")
Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
  Use `force = TRUE` to force installation
library(ggcorrplot)

# Creating matrix - natural log transformed
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_LN",
                                     TRAITS.BIN, TRAITS.CON, 
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers_serum <- round(cor(AEDB.CEA.matrix.serum, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers

corr_biomarkers_serum_p <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum, use = "pairwise.complete.obs", method = "spearman")
Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_serum_p, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_rank", 
                                     TRAITS.BIN, TRAITS.CON.RANK, 
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers_serum.rank <- round(cor(AEDB.CEA.matrix.serum.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_serum_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum.RANK, use = "pairwise.complete.obs", method = "spearman")
Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_serum_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))



# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers_serum.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum, corr_biomarkers_serum_p))
DT::datatable(corr_biomarkers_serum.df)


corr_biomarkers_serum.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum.rank, corr_biomarkers_serum_p.rank))
DT::datatable(corr_biomarkers_serum.rank.df)


# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AEDB.CEA.matrix.serum, method = "spearman", histogram = TRUE, pch = 3)


chart.Correlation.new(AEDB.CEA.matrix.serum.RANK, method = "spearman", histogram = TRUE, pch = 3)



# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA,
        columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"), 
        columnLabels = c("MCP1 (serum)", 
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 
Extra arguments: 'method' are being ignored.  If these are meant to be aesthetics, submit them using the 'mapping' variable within ggpairs with ggplot2::aes or ggplot2::aes_string.

Session information


Version:      v1.0.5
Last update:  2020-03-05
Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description:  Script to select samples from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin19.3.0 (64-bit)
Running under: macOS Catalina 10.15.3

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /usr/local/Cellar/openblas/0.3.7/lib/libopenblasp-r0.3.7.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] tools     stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] Hmisc_4.3-1                Formula_1.2-3              lattice_0.20-40            survminer_0.4.6            survival_3.1-8            
 [6] GGally_1.4.0               PerformanceAnalytics_2.0.4 xts_0.12-0                 zoo_1.8-7                  ggcorrplot_0.1.3.999      
[11] labelled_2.2.2             openxlsx_4.1.4             ggpubr_0.2.5.999           magrittr_1.5               tableone_0.11.0           
[16] haven_2.2.0                Seurat_3.1.4               devtools_2.2.2             usethis_1.5.1              MASS_7.3-51.5             
[21] DT_0.12                    knitr_1.28                 forcats_0.5.0              stringr_1.4.0              purrr_0.3.3               
[26] tibble_2.1.3               ggplot2_3.2.1              tidyverse_1.3.0            data.table_1.12.8          naniar_0.5.0              
[31] tidyr_1.0.2                dplyr_0.8.4                optparse_1.6.4             readr_1.3.1               

loaded via a namespace (and not attached):
  [1] rappdirs_0.3.1      visdat_0.5.3        acepack_1.4.1       irlba_2.3.3         multcomp_1.4-12     rpart_4.1-15       
  [7] inline_0.3.15       generics_0.0.2      metap_1.3           BiocGenerics_0.30.0 callr_3.4.2         cowplot_1.0.0      
 [13] TH.data_1.0-10      RANN_2.6.1          future_1.16.0       mutoss_0.1-12       xml2_1.2.2          lubridate_1.7.4    
 [19] httpuv_1.5.2        StanHeaders_2.19.2  assertthat_0.2.1    xfun_0.12           hms_0.5.3           evaluate_0.14      
 [25] promises_1.1.0      fansi_0.4.1         caTools_1.18.0      dbplyr_1.4.2        readxl_1.3.1        km.ci_0.5-2        
 [31] igraph_1.2.4.2      DBI_1.1.0           htmlwidgets_1.5.1   reshape_0.8.8       stats4_3.6.3        ellipsis_0.3.0     
 [37] crosstalk_1.0.0     backports_1.1.5     survey_3.37         gbRd_0.4-11         RcppParallel_4.4.4  vctrs_0.2.3        
 [43] Biobase_2.44.0      remotes_2.1.1       ROCR_1.0-7          withr_2.1.2         packrat_0.5.0       checkmate_2.0.0    
 [49] sctransform_0.2.1   prettyunits_1.1.1   getopt_1.20.3       mnormt_1.5-6        cluster_2.1.0       ape_5.3            
 [55] lazyeval_0.2.2      crayon_1.3.4        pkgconfig_2.0.3     labeling_0.3        nlme_3.1-145        pkgload_1.0.2      
 [61] nnet_7.3-13         rlang_0.4.5         globals_0.12.5      lifecycle_0.1.0     sandwich_2.5-1      modelr_0.1.6       
 [67] rsvd_1.0.3          cellranger_1.1.0    rprojroot_1.3-2     matrixStats_0.55.0  lmtest_0.9-37       Matrix_1.2-18      
 [73] loo_2.2.0           KMsurv_0.1-5        reprex_0.3.0        base64enc_0.1-3     ggridges_0.5.2      processx_3.4.2     
 [79] png_0.1-7           viridisLite_0.3.0   bitops_1.0-6        KernSmooth_2.23-16  jpeg_0.1-8.1        ggsignif_0.6.0     
 [85] scales_1.1.0        memoise_1.1.0       plyr_1.8.6          ica_1.0-2           gplots_3.0.3        bibtex_0.4.2       
 [91] gdata_2.18.0        compiler_3.6.3      lsei_1.2-0          RColorBrewer_1.1-2  plotrix_3.7-7       fitdistrplus_1.0-14
 [97] cli_2.0.2           listenv_0.8.0       patchwork_1.0.0     pbapply_1.4-2       ps_1.3.2            htmlTable_1.13.3   
[103] tidyselect_1.0.0    stringi_1.4.6       mitools_2.4         yaml_2.2.1          latticeExtra_0.6-29 ggrepel_0.8.1      
[109] survMisc_0.5.5      grid_3.6.3          future.apply_1.4.0  parallel_3.6.3      rstudioapi_0.11     foreign_0.8-76     
[115] gridExtra_2.3       farver_2.0.3        Rtsne_0.15          digest_0.6.25       shiny_1.4.0         quadprog_1.5-8     
[121] Rcpp_1.0.3          broom_0.5.5         later_1.0.0         RcppAnnoy_0.0.15    httr_1.4.1          rsconnect_0.8.16   
[127] npsurv_0.4-0        Rdpack_0.11-1       colorspace_1.4-1    rvest_0.3.5         fs_1.3.1            reticulate_1.14    
[133] splines_3.6.3       uwot_0.1.5          sn_1.5-5            multtest_2.40.0     plotly_4.9.2        sessioninfo_1.1.1  
[139] xtable_1.8-4        jsonlite_1.6.1      rstan_2.19.3        testthat_2.3.2      R6_2.4.1            TFisher_0.2.0      
[145] pillar_1.4.3        htmltools_0.4.0     mime_0.9            glue_1.3.1          fastmap_1.0.1       class_7.3-15       
[151] codetools_0.2-16    pkgbuild_1.0.6      tsne_0.1-3          mvtnorm_1.1-0       numDeriv_2016.8-1.1 curl_4.3           
[157] leiden_0.3.3        gtools_3.8.1        zip_2.0.4           rmarkdown_2.1       desc_1.2.0          munsell_0.5.0      
[163] e1071_1.7-3         reshape2_1.4.3      gtable_0.3.0       

Saving environment

save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))
© 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | swvanderlaan.github.io.
---
title: "Athero-Express Biobank Study -- IL6 and MCP1 plaque levels."
author: '[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans'
date: '`r Sys.Date()`'
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
  html_document:
    df_print: paged
    toc: yes
mainfont: Helvetica
subtitle: An 'Athero-Express Biobank Study' project
editor_options:
  chunk_output_type: inline
---
```{r global_options, include = FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
                      eval = TRUE, warning = FALSE, message = FALSE)
```

# Preparation

Clean the environment.
```{r ClearEnvironment, include = FALSE}
rm(list = ls())
```

Set locations, and the working directory.
```{r LocalSystem, include = FALSE}
### Operating System Version
### Mac Pro
# ROOT_loc = "/Volumes/EliteProQx2Media"
# GENOMIC_loc = "/Users/svanderlaan/iCloud/Genomics"

### MacBook
ROOT_loc = "/Users/swvanderlaan"
GENOMIC_loc = paste0(ROOT_loc, "/iCloud/Genomics")

### Generic Locations
AEDB_loc = paste0(GENOMIC_loc, "/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
RESULTS = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")
RAWDATA = paste0(ROOT_loc, "/PLINK/_AE_ORIGINALS/AESCRNA/prepped_data")
PROJECT_loc = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")

### SOME VARIABLES WE NEED DOWN THE LINE
cat("\nDefining phenotypes and datasets.\n")
PROJECTNAME="IL6MCP1"
# SUBPROJECTNAME=""

cat("\nCreate a new analysis directory, including subdirectories.\n")
# Analysis
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

# Plots
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

# QC plots
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

# Output files
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

# COX analysis
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/COX")), 
       dir.create(file.path(ANALYSIS_loc, "/COX")), 
       FALSE)
COX_loc = paste0(ANALYSIS_loc, "/COX")

# Baseline characteristics
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")

# Sample selection
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       dir.create(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       FALSE)
SELECTIONS_loc = paste0(ANALYSIS_loc, "/SELECTIONS")

cat("\nSetting working directory and listing its contents.\n")
setwd(paste0(PROJECT_loc))
getwd()
list.files()
```

A package-installation function.
```{r Function: installations, include = FALSE}
install.packages.auto <- function(x) { 
  x <- as.character(substitute(x)) 
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else { 
    # Update installed packages - this may mean a full upgrade of R, which in turn
    # may not be warrented. 
    # update.install.packages.auto(ask = FALSE) 
    eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"https://cloud.r-project.org/\")", x)))
  }
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else {
    if (!requireNamespace("BiocManager"))
      install.packages("BiocManager")
    # BiocManager::install() # this would entail updating installed packages, which in turned may not be warrented
    eval(parse(text = sprintf("BiocManager::install(\"%s\")", x)))
    eval(parse(text = sprintf("require(\"%s\")", x)))
  }
}
```

Load those packages.
```{r Setting: loading_packages, include = FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("MASS")
# install.packages.auto("Seurat") # latest version

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

install.packages.auto("haven")
install.packages.auto("tableone")

install.packages.auto("ggpubr")

```

We will create a datestamp and define the Utrecht Science Park Colour Scheme.
```{r Setting: Colors, include = FALSE}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
### 
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

#ggplot2 default color palette
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

### ----------------------------------------------------------------------------
```


```{r Analysis Functions}
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    tvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    R = summary(fit)$r.squared
    R.adj = summary(fit)$adj.r.squared
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
    
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("T-value...................:", round(tvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("R^2.......................:", round(R, 6), "\n")
    cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,9))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data...\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    zvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    dev <- fit$deviance
    nullDev <- fit$null.deviance
    modelN <- length(fit$fitted.values)
    R.l <- 1 - dev / nullDev
    R.cs <- 1 - exp(-(nullDev - dev) / modelN)
    R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("Z-value...................:", round(zvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
    cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
    cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}
```


# Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b).
While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.


## Objectives

Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.


## Methods

*Blood*

- IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]
- MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. Measured at the WKZ. Recalculated Luminex. [pg/mL]


*Plaque*

- IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug], measured by Luminex at the WKZ.
- MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug], measured by Luminex at the WKZ.
- IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug], measured by Luminex at the WKZ.


# Loading data

## Clinical data

Loading Athero-Express clinical data.
```{r LoadAEDB}
require(haven)

AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))

head(AEDB)

require(openxlsx)
AEDB_ProtConc <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_Vriezer/20190919_Freezers_preTCBio.xlsx"), 
                                     sheet = "ProteinConc.", 
                                     skipEmptyCols = TRUE)

AEDB_Blood <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_Vriezer/20190919_Freezers_preTCBio.xlsx"), 
                                     sheet = "Blood", 
                                     skipEmptyCols = TRUE)
AEDB_Plaque <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_Vriezer/20190919_Freezers_preTCBio.xlsx"), 
                                     sheet = "Plaque", 
                                     skipEmptyCols = TRUE)

head(AEDB_ProtConc)
head(AEDB_Blood)
head(AEDB_Plaque)


```

## Fixing and creating variables

We need to be very strict in defining _symptoms._ Therefore we will fix a new variable that groups _symptoms_ at inclusion.

Coding of _symptoms_ is as follows:

- missing	-999	
- Asymptomatic	0	
- TIA	1	
- minor stroke	2	
- Major stroke	3	
- Amaurosis fugax	4	
- Four vessel disease	5	
- Vertebrobasilary TIA	7	
- Retinal infarction	8	
- Symptomatic, but aspecific symtoms	9
- Contralateral symptomatic occlusion	10	
- retinal infarction	11	
- armclaudication due to occlusion subclavian artery, CEA needed for bypass	12	
- retinal infarction + TIAs	13	
- Ocular ischemic syndrome	14	
- ischemisch glaucoom	15	
- subclavian steal syndrome	16	
- TGA	17

We will group as follows in `Symptoms.5G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13
3. Stroke > 2, 3
4. Ocular > 4, 14, 15
5. Retinal infarction > 8, 11
6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3
3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17

```{r FixSymptoms}
# Fix symptoms
attach(AEDB)
# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"
detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)

rm(AEDB.temp)
```

We will also fix the _plaquephenotypes_ variable.  

Coding of symptoms is as follows:

- missing	-999	
- not relevant -888
- fibrous	1	
- fibroatheromatous	2	
- atheromatous	3	


```{r FixPlaquePhenotypes}

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)


```

We will also fix the _diabetes_ status variable.


```{r FixDiabetes}

# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)


```

# Athero-Express Biobank Study

## Baseline characteristics

```{r Baseline AEDB: creation, include = FALSE}
cat("====================================================================================================\n")
cat("SELECTION THE SHIZZLE\n")

### Artery levels
# AEdata$Artery_summary: 
#           value                                                                                   label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1                                                                  carotid (left & right)
# USE - 2                                               femoral/iliac (left, right or both sides)
# NOT USE - 3                                               other carotid arteries (common, external)
# NOT USE - 4                                   carotid bypass and injury (left, right or both sides)
# NOT USE - 5                                                         aneurysmata (carotid & femoral)
# NOT USE - 6                                                                                   aorta
# NOT USE - 7                                            other arteries (renal, popliteal, vertebral)
# NOT USE - 8                        femoral bypass, angioseal and injury (left, right or both sides)

### AEdata$informedconsent
#           value                                                                                           label
# NOT USE - -999                                                                                         missing
# NOT USE - 0                                                                                        no, died
# USE - 1                                                                                             yes
# USE - 2                                                             yes, health treatment when possible
# USE - 3                                                                        yes, no health treatment
# USE - 4                                                yes, no health treatment, no commercial business
# NOT USE - 5                                                          yes, no tissue, no commerical business
# NOT USE - 6                      yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7                             yes, no questionnaires, no health treatment, no commercial business
# USE - 8                                          yes, no questionnaires, health treatment when possible
# NOT USE - 9                  yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10                               yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12                                                     yes, no questionnaires, no health treatment
# NOT USE - 13                                                             yes, no tissue, no health treatment
# NOT USE - 14                                                               yes, no tissue, no questionnaires
# NOT USE - 15                                                  yes, no tissue, health treatment when possible
# NOT USE - 16                                                                                  yes, no tissue
# USE - 17                                                                     yes, no commerical business
# USE - 18                                     yes, health treatment when possible, no commercial business
# USE - 19                                                    yes, no medical info, no commercial business
# USE - 20                                                                          yes, no questionnaires
# NOT USE - 21                         yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22                  yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23                                                                            yes, no medical info
# USE - 24                                                  yes, no questionnaires, no commercial business
# USE - 25                                    yes, no questionnaires, no health treatment, no medical info
# USE - 26                  yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27                                                      yes,  no health treatment, no medical info
# NOT USE - 28                                                                             no, doesn't want to
# NOT USE - 29                                                                              no, unable to sign
# NOT USE - 30                                                                                 no, no reaction
# NOT USE - 31                                                                                        no, lost
# NOT USE - 32                                                                                     no, too old
# NOT USE - 34                                            yes, no medical info, health treatment when possible
# NOT USE - 35                                             no (never asked for IC because there was no tissue)
# USE - 36                    yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37                                                                                    no, endpoint
# USE - 38                                                         wil niets invullen, wel alles gebruiken
# USE - 39                                           second informed concents: yes, no commercial business
# NOT USE - 40                                                                              nooit geincludeerd

cat("- sanity checking PRIOR to selection")
library(data.table)
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
ae.hospital <- ifelse(AEDB$Hospital == 1, "Antonius", "UMCU")
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
table(ae.gender, AEDB$Artery_summary, dnn = c("Sex", "Artery"))
table(ae.gender, AEDB$informedconsent, dnn = c("Sex", "IC"))

rm(ae.gender, ae.hospital)

# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)
AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)
AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)
AEDB$IL6 <- as.numeric(AEDB$IL6)
AEDB$IL6_pg_ug_2015 <- as.numeric(AEDB$IL6_pg_ug_2015)
AEDB$IL6R_pg_ug_2015 <- as.numeric(AEDB$IL6R_pg_ug_2015)
AEDB$MCP1 <- as.numeric(AEDB$MCP1)
AEDB$MCP1_pg_ug_2015 <- as.numeric(AEDB$MCP1_pg_ug_2015)
AEDB$hsCRP_plasma <- as.numeric(AEDB$hsCRP_plasma)

require(labelled)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)
AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)
AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)

AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)
AEDB$informedconsent <- to_factor(AEDB$informedconsent)
AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)

AEDB.CEA <- subset(AEDB,
                    (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                       informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd")
AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)

cat("===========================================================================================\n")
cat("CREATE BASELINE TABLE\n")

# Baseline table variables
basetable_vars = c("Hospital", 
                   "Age", "Gender", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
                   "DiabetesStatus", "Hypertension.composite", 
                   "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt",
                   "restenos", "stenose",
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6_pg_ug_2015", "MCP1_pg_ug_2015")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerCurrent", 
                  "DiabetesStatus", "Hypertension.composite", 
                  "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt",
                  "restenos", "stenose",
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

Showing the baseline table of the whole Athero-Express Biobank.
```{r Baseline AEDB: Visualize}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]

AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(IL6_pg_ug_2015) | !is.na(MCP1_pg_ug_2015))

AEDB.CEA.subset.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "DiabetesStatus",
                                         data = AEDB.CEA.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]

AEDB.CEA.subset.serum <- subset(AEDB.CEA, !is.na(IL6) | !is.na(MCP1))

AEDB.CEA.subset.serum.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "DiabetesStatus",
                                         data = AEDB.CEA.subset.serum, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]
```

Writing the baseline table to Excel format. 
```{r Baseline AEDB: write}
# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
           AEDB.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
           AEDB.CEA.subset.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "subsetAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAserum.xlsx"),
           AEDB.CEA.subset.serum.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "subsetAEDB_Baseline_serum")
```

# Athero-Express Genomics Study (AEGS)

We will add the samples which were genotyped in the Athero-Express Biobank Study, _i.e._ Athero-Express Genomics Study 1 (AEGS1, Affymetrix SNP 5.0), AEGS2 (Affymetrix Axiom CEU), and AEGS3 (Illumina GSA).

## Loading AEGS data 

```{r create AEGS}
AEGS1_2_3 <- fread(paste0(ROOT_loc,"/PLINK/_AE_Originals/AEGS_COMBINED_QC2018/aegs1_2_3_combo_postqcmichimp_n2493.forR.txt"),
                   verbose = TRUE, showProgress = TRUE)
AEGS1_2_3$Study_Number <- as.numeric(AEGS1_2_3$Study_Number)
AEGS1_2_3$Age <- NULL
dim(AEGS1_2_3)
head(AEGS1_2_3)

AEGS_raw <- merge(AEGS1_2_3, AEDB, by.x = "Study_Number", by.y = "STUDY_NUMBER", sort = FALSE,
                  all.x = TRUE)

dim(AEGS_raw)
warnings() 

```


Here we will subset only those genotyped samples that passed genotyping quality control, are unrelated, and have informed consent.
```{r SpecificSelection}
AEGS_raw$Artery_summary <- to_factor(AEGS_raw$Artery_summary)
AEGS_raw$informedconsent <- to_factor(AEGS_raw$informedconsent)
table(AEGS_raw$Artery_summary, AEGS_raw$QC2018_FILTER)
table(AEGS_raw$informedconsent, AEGS_raw$QC2018_FILTER)
AEGSselect <- subset(AEGS_raw, 
                     QC2018_FILTER != "issue" & QC2018_FILTER != "family_discard" &
                       (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & 
                       informedconsent != "missing" &
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd")
dim(AEGSselect)

table(AEGSselect$Artery_summary, AEGSselect$QC2018_FILTER)
table(AEGSselect$Artery_summary, AEGSselect$CHIP)
table(AEGSselect$QC2018_FILTER, AEGSselect$CHIP)
table(AEGSselect$QC2018_FILTER, AEGSselect$SAMPLE_TYPE)

AEDB.temp <- subset(AEGSselect,  select = c("Study_Number", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "QC2018_FILTER", "CHIP", "SAMPLE_TYPE"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
AEDB.temp$QC2018_FILTER <- to_factor(AEDB.temp$QC2018_FILTER)
AEDB.temp$CHIP <- to_factor(AEDB.temp$CHIP)
AEDB.temp$SAMPLE_TYPE <- to_factor(AEDB.temp$SAMPLE_TYPE)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

```

## Baseline characteristics
Showing the baseline table of the whole Athero-Express Genomics Study.
```{r Baseline SampleSelect: Visualize}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html

basetable_vars_geno = c("Hospital", 
                   "Age", "Gender", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
                   "DiabetesStatus", "Hypertension.composite", 
                   "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt",
                   "restenos", "stenose",
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
                   "QC2018_FILTER", "CHIP", "SAMPLE_TYPE")

basetable_bin_geno = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerCurrent", 
                  "DiabetesStatus", "Hypertension.composite", 
                  "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt",
                  "restenos", "stenose",
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                  "QC2018_FILTER", "CHIP", "SAMPLE_TYPE")

basetable_con_geno = basetable_vars_geno[!basetable_vars_geno %in% basetable_bin_geno]

AEGSselect.tableOne = print(CreateTableOne(vars = basetable_vars_geno, 
                                         # factorVars = basetable_bin,
                                         # strata = "DiabetesStatus",
                                         data = AEGSselect, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]

AEGSselect.subset <- subset(AEGSselect, !is.na(IL6R_pg_ug_2015) | !is.na(MCP1_pg_ug_2015))

AEGSselect.subset.tableOne = print(CreateTableOne(vars = basetable_vars_geno, 
                                         # factorVars = basetable_bin,
                                         # strata = "DiabetesStatus",
                                         data = AEGSselect.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:2]

```

Let's also save these baseline tables.
```{r Baseline SampleSelection: write}
# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AEGS.BaselineTable.xlsx"), 
           AEGSselect.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "AEGS_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AEGS.BaselineTable.subset.xlsx"), 
           AEGSselect.subset.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "AEGS_Baseline_subset")

```


## Data exploration

Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation. 

```{r DataExploration: IL6 plaque}

summary(AEDB.CEA$IL6_pg_ug_2015)

ggpubr::gghistogram(AEDB.CEA, "IL6_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())

min_IL6pg_ug_2015 <- min(AEDB.CEA$IL6_pg_ug_2015, na.rm = TRUE)
min_IL6pg_ug_2015

AEDB.CEA$IL6_pg_ug_2015_LN <- log(AEDB.CEA$IL6_pg_ug_2015 + min_IL6pg_ug_2015)

ggpubr::gghistogram(AEDB.CEA, "IL6_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$IL6_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$IL6_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$IL6_pg_ug_2015)))
ggpubr::gghistogram(AEDB.CEA, "IL6_pg_ug_2015_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 plaque levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
```

```{r DataExploration: IL6R plaque}

summary(AEDB.CEA$IL6R_pg_ug_2015)

ggpubr::gghistogram(AEDB.CEA, "IL6R_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6R plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())

min_IL6R_pg_ug_2015 <- min(AEDB.CEA$IL6R_pg_ug_2015, na.rm = TRUE)
min_IL6R_pg_ug_2015

AEDB.CEA$IL6R_pg_ug_2015_LN <- log(AEDB.CEA$IL6R_pg_ug_2015 + min_IL6R_pg_ug_2015)
ggpubr::gghistogram(AEDB.CEA, "IL6R_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6R plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$IL6R_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$IL6R_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$IL6R_pg_ug_2015)))
ggpubr::gghistogram(AEDB.CEA, "IL6R_pg_ug_2015_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6R plaque levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
```

```{r DataExploration: MCP1 plaque}

summary(AEDB.CEA$MCP1_pg_ug_2015)

ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())

min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015

AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
```

```{r DataExploration: IL6 serum}

summary(AEDB.CEA$IL6)

ggpubr::gghistogram(AEDB.CEA, "IL6", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 serum levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())

min_IL6 <- min(AEDB.CEA$IL6, na.rm = TRUE)
min_IL6

AEDB.CEA$IL6_LN <- log(AEDB.CEA$IL6 + min_IL6)
ggpubr::gghistogram(AEDB.CEA, "IL6_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 serum levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$IL6_rank <- qnorm((rank(AEDB.CEA$IL6, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$IL6)))
ggpubr::gghistogram(AEDB.CEA, "IL6_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "IL6 serum levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
```

```{r DataExploration: MCP1 serum}

summary(AEDB.CEA$MCP1)

ggpubr::gghistogram(AEDB.CEA, "MCP1", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())

min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1

AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
ggpubr::gghistogram(AEDB.CEA, "MCP1_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
ggpubr::gghistogram(AEDB.CEA, "MCP1_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 serum levels",
                    xlab = "inverse-normal transformation pg/ug", 
                    ggtheme = theme_minimal())
```

## Preliminary conclusion data exploration

After discussion we decided to pursuit the following strategy. In line with the previous work by [Marios Georgakis](https://www.ahajournals.org/doi/full/10.1161/CIRCRESAHA.119.315380){target="_blank"} we will apply _natural log transformation_ on all proteins and focus the analysis on:

- MCP1 (serum and plaque), 
- IL6 (serum and plaque), and 
- IL6R (in plaque).

# Analyses

The analyses are focused on three elements: 

1) plaque vulnerability phenotypes
2) clinical status at inclusion (symptoms)
3) secondary clinical outcome during three (3) years of follow-up

## Covariates & other variables

1.  Age (continuous in 1-year increment). [Age]
2.  Sex (male vs. female). [Gender]
3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
5.  Smoking (current, ex-, never). [SmokerCurrent]
6.  LDL-C levels (continuous). [LDL_final]
7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
9.  eGFR (continuous). [GFR_MDRD]
10.	BMI (continuous). [BMI]
11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [CAD_history, Stroke_history, Peripheral.interv]
12.	Level of stenosis (50-70% vs. 70-99%). [stenose]
13.	Presenting symptoms (asymptomatic, ocular, TIA, or stroke). [Symptoms.5G]
14.	hsCRP circulating levels (ln-transformed, continuous). [hsCRP_plasma]
15.	IL-6 plaque levels (ln-transformed, continuous). [IL6_pg_ug_2015_LN]

## Models

We will analyze the data through four different models

- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
- Model 3: same to model 2, with additional adjustments for circulating CRP levels
- Model 4: same to model 2 with additional adjustment for IL6 levels in the plaque

## A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque and serum MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:

- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)

*Continous traits*
```{r CrossSec: plaques - transformations and visualisations continuous}

# macrophages
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)

ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# smooth muscle cells
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)

ggpubr::gghistogram(AEDB.CEA, "SMC_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# vessel density
cat("Summary of data.\n")
summary(AEDB.CEA$vessel_density_averaged)

min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)

ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                    xlab = "natural log-transformed number", 
                    ggtheme = theme_minimal())

AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
```

*Binary traits*
```{r CrossSec: plaques - transformations and visualisations binary}

# calcification
cat("Summary of data.\n")
summary(AEDB.CEA$Calc.bin)
contrasts(AEDB.CEA$Calc.bin)

AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CalcificationPlaque)) %>%
  group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())
rm(df)

# collagen
cat("Summary of data.\n")
summary(AEDB.CEA$Collagen.bin)
contrasts(AEDB.CEA$Collagen.bin)

AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CollagenPlaque)) %>%
  group_by(Gender, CollagenPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())
rm(df)

# fat 10%
cat("Summary of data.\n")
summary(AEDB.CEA$Fat.bin_10)
contrasts(AEDB.CEA$Fat.bin_10)

AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)

df <- AEDB.CEA %>%
  filter(!is.na(Fat10Perc)) %>%
  group_by(Gender, Fat10Perc) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())
rm(df)

# IPH
cat("Summary of data.\n")
summary(AEDB.CEA$IPH.bin)
contrasts(AEDB.CEA$IPH.bin)

AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)

df <- AEDB.CEA %>%
  filter(!is.na(IPH)) %>%
  group_by(Gender, IPH) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())
rm(df)

# Symptoms
cat("Summary of data.\n")
summary(AEDB.CEA$AsymptSympt)
contrasts(AEDB.CEA$AsymptSympt)

AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)

df <- AEDB.CEA %>%
  filter(!is.na(AsymptSympt)) %>%
  group_by(Gender, AsymptSympt) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())
rm(df)

```

In this section we make some variables to assist with analysis.
```{r CrossSec: plaques - setup regression }
AEDB.CEA.samplesize = nrow(AEDB.CEA)
TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")

TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN") 
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")

TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH")

# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.	BMI (continuous). [BMI]
# 11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [CAD_history, Stroke_history, Peripheral.interv]
# 12.	Level of stenosis (50-70% vs. 70-99%). [stenose]
# 13.	Presenting symptoms (asymptomatic, ocular, TIA, or stroke). [Symptoms.5G]
# 14.	hsCRP circulating levels (ln-transformed, continuous). [hsCRP_plasma]
# 15.	IL-6 plaque levels (ln-transformed, continuous). [IL6_pg_ug_2015_LN]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
# Model 3: same to model 2, with additional adjustments for circulating CRP levels
# Model 4: same to model 2 with additional adjustment for IL6 levels in the plaque

COVARIATES_M1 = c("Age", "Gender")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               "CAD_history", "Stroke_history", "Peripheral.interv",
               "stenose")

COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")

```

### Model 1

In this model we correct for _Age_ and _Gender_.


#### Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL1, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL1, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, and _stenosis_.

#### Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL2}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL2, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 3

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, _stenosis_, and _LDL_.

#### Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL3, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL3, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL3 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL3 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 4

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, _stenosis_, and _hsCRP_.

#### Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL4, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL4, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL4 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL4 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 5

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, _stenosis_, and _IL6 in plaques_.

#### Natural log-transformed data

First we use the natural-log transformed data.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL5, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON)) {
    TRAIT = TRAITS.CON[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_LN, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL5, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_LN, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of continuous/quantitative plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL5 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5rank) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

Analysis of binary plaque traits as a function of serum/plaque IL6(R)/MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL5 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5rank) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + 
                Hypertension.composite + DiabetesStatus + SmokerCurrent + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque and serum MCP1, IL6, and IL6R levels and the 'clinical status' of the plaque in terms of presence of patients' symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic

### Model 1

In this model we correct for _Age_, and _Gender_.

#### Natural log-transformed data

First we use the natural-log transformed data.

```{r CrossSec: symptoms - logistic regression MODEL1, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

```{r CrossSec: symptoms - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, and _stenosis._.

#### Natural log-transformed data

First we use the natural-log transformed data.

```{r CrossSec: symptoms - logistic regression MODEL2, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

```{r CrossSec: symptoms - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 3

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, _stenosis._, and _LDL_.

#### Natural log-transformed data

First we use the natural-log transformed data.

```{r CrossSec: symptoms - logistic regression MODEL3, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.Symptoms.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

```{r CrossSec: symptoms - logistic regression MODEL3 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M3) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + LDL_final, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL3.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 4

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, _stenosis._, and _hsCRP_.

#### Natural log-transformed data

First we use the natural-log transformed data.

```{r CrossSec: symptoms - logistic regression MODEL4, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

```{r CrossSec: symptoms - logistic regression MODEL4 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M4) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL4.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 5

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _CAD history_, _stroke history_, _peripheral interventions_, _stenosis._, and _IL6 in plaques_.

#### Natural log-transformed data

First we use the natural-log transformed data.

```{r CrossSec: symptoms - logistic regression MODEL5, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN)) {
  PROTEIN = TRAITS.PROTEIN[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_LN, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

#### Inverse-rank normalization

Here we use the inverse-rank normalized data - visually this is more normally distributed.

```{r CrossSec: symptoms - logistic regression MODEL5 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M5rank) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
                CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL5.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque and serum MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period. 

The _primary outcome_ is defined as "a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death", i.e. major adverse cardiovascular events (MACE). Variable: `epmajor.3years`, these include:
- myocardial infarction (MI)
- cerebral infarction (CVA/stroke)
- cardiovascular death (exact cause to be investigated)
- cerebral bleeding (CVA/stroke)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..

The _secondary outcomes_ will be 

- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: `epstroke.3years`, these include:
  - cerebral infarction (CVA/stroke)
  - cerebral bleeding (CVA/stroke)
  - fatal cerebral infarction
  - fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: `epcoronary.3years`, these include:
  - myocardial infarction (MI)
  - coronary angioplasty (PCI/PTCA)
  - cardiovascular death (exact cause to be investigated)
  - coronary bypass (CABG)
  - fatal myocardial infarction (MI)
  - sudden death.
- cardiovascular death - variable: `epcvdeath.3years`, these include:
  - cardiovascular death (exact cause to be investigated)
  - fatal myocardial infarction (MI)
  - fatal cerebral infarction
  - fatal cerebral bleeding
  - sudden death
  - fatal heart failure
  - fatal aneurysm rupture
  - other cardiovascular death..

### Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.

```{r Cox-regressions: General}
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
  cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(protein, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", protein, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints){
  print(paste0("Printing the summary of: ",events))
  print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}


```


### Cox regressions

Let's perform the actual Cox-regressions. We will apply a couple of models: 

- Model 1: adjusted for age and sex
- Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
- Model 3: same to model 2, with additional adjustments for circulating CRP levels
- Model 4: same to model 2 with additional adjustment for IL6 levels in the plaque


*MODEL 1*
```{r Cox-regression Analysis: Simple model}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```

*MODEL 3*
```{r Cox-regression Analysis: MODEL 3}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 3 same to model 2, with additional adjustments for circulating CRP levels
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent +  Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + hsCRP_plasma, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))

    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL3.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL3.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```

*MODEL 4*
```{r Cox-regression Analysis: MODEL 4}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 4 same to model 2 with additional adjustment for IL6 levels in the plaque
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + Hypertension.composite + DiabetesStatus + SmokerCurrent + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + CAD_history + Stroke_history + Peripheral.interv + stenose + IL6_pg_ug_2015_rank, data = TEMP.DF)
  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL4.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL4.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```




# Correlations

## All biomarkers
We correlated serum and plaque levels of the biomarkers.

```{r CrossSampleType Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - natural log transformed
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN",
                                     TRAITS.BIN, TRAITS.CON)
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)
AEDB.CEA.matrix <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers <- round(cor(AEDB.CEA.matrix, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers

corr_biomarkers_p <- ggcorrplot::cor_pmat(AEDB.CEA.matrix, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
                                               TRAITS.BIN, TRAITS.CON.RANK)
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.df <- as.data.table(flattenCorrMatrix(corr_biomarkers, corr_biomarkers_p))
DT::datatable(corr_biomarkers.df)

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AEDB.CEA.matrix, method = "spearman", histogram = TRUE, pch = 3)

chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA, 
        columns = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK), 
        columnLabels = c("IL6 (serum)", "MCP1 (serum)", "IL6", "MCP1", "IL6R",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 
```

## Circulating MCP1

Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:

1. A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
2. Associations of circulating MCP-1 levels with plaque vulnerability characteristics
3. Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
4. Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.

```{r MCP1 Serum Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - natural log transformed
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_LN",
                                     TRAITS.BIN, TRAITS.CON, 
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers_serum <- round(cor(AEDB.CEA.matrix.serum, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers

corr_biomarkers_serum_p <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_serum_p, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_rank", 
                                     TRAITS.BIN, TRAITS.CON.RANK, 
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.serum.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers_serum.rank <- round(cor(AEDB.CEA.matrix.serum.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_serum_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.serum.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_serum.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_serum_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))


# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers_serum.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum, corr_biomarkers_serum_p))
DT::datatable(corr_biomarkers_serum.df)

corr_biomarkers_serum.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_serum.rank, corr_biomarkers_serum_p.rank))
DT::datatable(corr_biomarkers_serum.rank.df)

# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AEDB.CEA.matrix.serum, method = "spearman", histogram = TRUE, pch = 3)

chart.Correlation.new(AEDB.CEA.matrix.serum.RANK, method = "spearman", histogram = TRUE, pch = 3)


# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA,
        columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"), 
        columnLabels = c("MCP1 (serum)", 
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 
```


# Session information

------

    Version:      v1.0.5
    Last update:  2020-03-05
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to select samples from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).

------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))
```

------
<sup>&copy; 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup>
------

